付属資料（Rによる経済予測シミュレーション）.ipynb

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   "source": [
    "# 付属資料（Rによる経済予測シミュレーション）\n",
    "\n",
    "### 著者\n",
    "- 氏名：柴田 怜\n",
    "- 職業：データサイエンティスト\n",
    "- 学位：上智大学修士（環境学）\n",
    "\n",
    "### 前提\n",
    "1. メモリ容量の最大化\n",
    "1. 必要なライブラリの読込\n",
    "1. 関数定義\n",
    "1. ローデータの目視確認\n",
    "\n",
    "### 設計\n",
    "1. 誤差項調整\n",
    "1. 多重共線性の実証分析\n",
    "1. 無相関検定\n",
    "1. 単位根検定(ADF検定)\n",
    "1. 共和分検定\n",
    "1. 偏グレンジャー因果性検定と非直交化インパルス応答関数\n",
    "1. パネルVARモデルによる動的直接相関係数の導出\n",
    "1. 幾何ブラウン運動\n",
    "1. LSTM (Long Short Term Memory)\n",
    "1. グラフ描画・出力"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## メモリ容量の最大化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "vscode": {
     "languageId": "r"
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   "source": [
    "memory.limit(memory.size(max = T))\n",
    "gc(verbose = getOption(\"verbose\"), reset = T, full = T)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 必要なライブラリの読込"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "vscode": {
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      " 要求されたパッケージ magrittr をロード中です \n",
      "\n",
      " 要求されたパッケージ tidyr をロード中です \n",
      "\n",
      "\n",
      " 次のパッケージを付け加えます: 'tidyr' \n",
      "\n",
      "\n",
      " 以下のオブジェクトは 'package:magrittr' からマスクされています: \n",
      "\n",
      "     extract \n",
      "\n",
      "\n",
      " 要求されたパッケージ dplyr をロード中です \n",
      "\n",
      "\n",
      " 次のパッケージを付け加えます: 'dplyr' \n",
      "\n",
      "\n",
      " 以下のオブジェクトは 'package:data.table' からマスクされています: \n",
      "\n",
      "     between, first, last \n",
      "\n",
      "\n",
      " 以下のオブジェクトは 'package:stats' からマスクされています: \n",
      "\n",
      "     filter, lag \n",
      "\n",
      "\n",
      " 以下のオブジェクトは 'package:base' からマスクされています: \n",
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      "     intersect, setdiff, setequal, union \n",
      "\n",
      "\n",
      " 要求されたパッケージ tidyverse をロード中です \n",
      "\n",
      "-- \u001b[1mAttaching packages\u001b[22m ------------------------------------------------------------------------------- tidyverse 1.3.1 --\n",
      "\n",
      "\u001b[32mv\u001b[39m \u001b[34mggplot2\u001b[39m 3.3.3     \u001b[32mv\u001b[39m \u001b[34mpurrr  \u001b[39m 0.3.4\n",
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      "\n",
      "-- \u001b[1mConflicts\u001b[22m ---------------------------------------------------------------------------------- tidyverse_conflicts() --\n",
      "\u001b[31mx\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mbetween()\u001b[39m   masks \u001b[34mdata.table\u001b[39m::between()\n",
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      "\n",
      " 要求されたパッケージ tseries をロード中です \n",
      "\n",
      "Registered S3 method overwritten by 'quantmod':\n",
      "  method            from\n",
      "  as.zoo.data.frame zoo \n",
      "\n",
      " 要求されたパッケージ urca をロード中です \n",
      "\n",
      " 要求されたパッケージ aTSA をロード中です \n",
      "\n",
      "\n",
      " 次のパッケージを付け加えます: 'aTSA' \n",
      "\n",
      "\n",
      " 以下のオブジェクトは 'package:tseries' からマスクされています: \n",
      "\n",
      "     adf.test, kpss.test, pp.test \n",
      "\n",
      "\n",
      " 以下のオブジェクトは 'package:graphics' からマスクされています: \n",
      "\n",
      "     identify \n",
      "\n",
      "\n",
      " 要求されたパッケージ plm をロード中です \n",
      "\n",
      "\n",
      " 次のパッケージを付け加えます: 'plm' \n",
      "\n",
      "\n",
      " 以下のオブジェクトは 'package:dplyr' からマスクされています: \n",
      "\n",
      "     between, lag, lead \n",
      "\n",
      "\n",
      " 以下のオブジェクトは 'package:data.table' からマスクされています: \n",
      "\n",
      "     between \n",
      "\n",
      "\n",
      " 要求されたパッケージ panelvar をロード中です \n",
      "\n",
      "Welcome to panelvar! Please cite our package in your publications -- see citation(\"panelvar\")\n",
      "\n",
      "\n",
      " 次のパッケージを付け加えます: 'panelvar' \n",
      "\n",
      "\n",
      " 以下のオブジェクトは 'package:tidyr' からマスクされています: \n",
      "\n",
      "     extract \n",
      "\n",
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      " 以下のオブジェクトは 'package:magrittr' からマスクされています: \n",
      "\n",
      "     extract \n",
      "\n",
      "\n",
      " 要求されたパッケージ Sim.DiffProc をロード中です \n",
      "\n",
      "Package 'Sim.DiffProc', version 4.8\n",
      "browseVignettes('Sim.DiffProc') for more informations.\n",
      "\n",
      " 要求されたパッケージ gridExtra をロード中です \n",
      "\n",
      "\n",
      " 次のパッケージを付け加えます: 'gridExtra' \n",
      "\n",
      "\n",
      " 以下のオブジェクトは 'package:dplyr' からマスクされています: \n",
      "\n",
      "     combine \n",
      "\n",
      "\n",
      " 要求されたパッケージ qgraph をロード中です \n",
      "\n",
      " 要求されたパッケージ tsbox をロード中です \n",
      "\n",
      " 要求されたパッケージ keras をロード中です \n",
      "\n",
      " 要求されたパッケージ tensorflow をロード中です \n",
      "\n",
      " 要求されたパッケージ vars をロード中です \n",
      "\n",
      " 要求されたパッケージ MASS をロード中です \n",
      "\n",
      "\n",
      " 次のパッケージを付け加えます: 'MASS' \n",
      "\n",
      "\n",
      " 以下のオブジェクトは 'package:dplyr' からマスクされています: \n",
      "\n",
      "     select \n",
      "\n",
      "\n",
      " 要求されたパッケージ strucchange をロード中です \n",
      "\n",
      " 要求されたパッケージ zoo をロード中です \n",
      "\n",
      "\n",
      " 次のパッケージを付け加えます: 'zoo' \n",
      "\n",
      "\n",
      " 以下のオブジェクトは 'package:base' からマスクされています: \n",
      "\n",
      "     as.Date, as.Date.numeric \n",
      "\n",
      "\n",
      " 要求されたパッケージ sandwich をロード中です \n",
      "\n",
      "\n",
      " 次のパッケージを付け加えます: 'strucchange' \n",
      "\n",
      "\n",
      " 以下のオブジェクトは 'package:stringr' からマスクされています: \n",
      "\n",
      "     boundary \n",
      "\n",
      "\n",
      " 要求されたパッケージ lmtest をロード中です \n",
      "\n",
      "\n",
      " 次のパッケージを付け加えます: 'vars' \n",
      "\n",
      "\n",
      " 以下のオブジェクトは 'package:panelvar' からマスクされています: \n",
      "\n",
      "     stability \n",
      "\n",
      "\n",
      " 以下のオブジェクトは 'package:aTSA' からマスクされています: \n",
      "\n",
      "     arch.test \n",
      "\n",
      "\n",
      " 要求されたパッケージ NlinTS をロード中です \n",
      "\n",
      " 要求されたパッケージ Rcpp をロード中です \n",
      "\n",
      " 要求されたパッケージ tsDyn をロード中です \n",
      "\n"
     ]
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       "data.table\n",
       ":   TRUEmagrittr\n",
       ":   TRUEtidyr\n",
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       ":   TRUEtseries\n",
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       "        TRUE         TRUE         TRUE         TRUE         TRUE         TRUE \n",
       "        urca         aTSA          plm     panelvar Sim.DiffProc      ggplot2 \n",
       "        TRUE         TRUE         TRUE         TRUE         TRUE         TRUE \n",
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     "text": [
      "\n",
      "Installation complete.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "load.lib <- c(\n",
    " \"data.table\"     #.csv読込・出力\n",
    " , \"magrittr\"     #前処理\n",
    " , \"tidyr\"        #前処理\n",
    " , \"dplyr\"        #前処理\n",
    " , \"tidyverse\"    #前処理\n",
    " , \"tseries\"      #前処理\n",
    " , \"urca\"         #ADF検定\n",
    " , \"aTSA\"         #共和分検定\n",
    " , \"plm\"          #パネルデータの形成\n",
    " , \"panelvar\"     #パネルVARモデル\n",
    " , \"Sim.DiffProc\" #幾何ブラウン運動\n",
    " , \"ggplot2\"      #可視化\n",
    " , \"gridExtra\"    #グラフの集約\n",
    " , \"qgraph\"       #相関行列の可視化\n",
    " , \"tsbox\"        #ts_df関数の利用\n",
    " , \"keras\"        #LSTMによる幾何ブラウン運動の精度評価\n",
    " , \"tensorflow\"   #LSTMによる幾何ブラウン運動の精度評価\n",
    " , \"vars\"         #関数定義に用いる(以下、同じ)。\n",
    " , \"NlinTS\"\n",
    " , \"tsDyn\"\n",
    ")\n",
    "install.lib <- load.lib[!load.lib %in% installed.packages()]\n",
    "for(lib in install.lib) install.packages(lib,dependencies = T)\n",
    "sapply(load.lib,require,character = T)\n",
    "#LSTMによる幾何ブラウン運動の精度評価\n",
    "install_tensorflow(gpu = TRUE) #GPUの利用"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 関数定義\n",
    "- 対数差分系列\n",
    "- プロビット写像\n",
    "- 偏グレンジャー因果性検定と非直交化インパルス応答関数\n",
    "- ADF検定\n",
    "- 標本分散\n",
    "- 時系列プロット\n",
    "- LSTM (Long Short Term Memory)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "#対数差分系列に変換する。\n",
    "diff.log <- function(x) {\n",
    "  y <- diff(log(x))\n",
    "  return(y)\n",
    "}\n",
    "#プロビット写像\n",
    "probit <- function(x) {\n",
    "  y <- c(exp(((-x^2)/2))/sqrt(2 * pi))\n",
    "  return(y)\n",
    "}\n",
    "#偏グレンジャー因果性検定と非直交化インパルス応答関数\n",
    "ts <- function(y1, y2) {\n",
    "  temp  <- cbind(y1, y2) %>% as.data.frame\n",
    "  model <- VAR(temp, p = 2, type = \"both\", ic = \"AIC\")\n",
    "  wk_result_1  <- causality(model, cause = \"y1\")\n",
    "  wk_result_2  <- causality(model, cause = \"y2\")\n",
    "  granger      <- list(wk_result_1, wk_result_2)\n",
    "  impulse_1 <- irf(model, impulse = \"y1\", response = \"y2\", boot = F)\n",
    "  impulse_2 <- irf(model, impulse = \"y2\", response = \"y1\", boot = F)\n",
    "  imp <- list(impulse_1, impulse_2)\n",
    "  result <- list(granger, imp)\n",
    "  return(result)\n",
    "}\n",
    "#ADF検定\n",
    "ADF <- function(x) {\n",
    "  result <- ur.df(x,type = c(\"drift\"), lags = 1) %>%\n",
    "    summary\n",
    "  return(result)\n",
    "}\n",
    "#標本分散\n",
    "sigma <- function(x) {\n",
    "  result <- var(x)*(length(x)-1)/length(x)\n",
    "  return(result)\n",
    "}\n",
    "#時系列プロット\n",
    "fig <- function(data, y, title, label) {\n",
    "  data %>%\n",
    "    ggplot(aes(x = time, y = y)) +\n",
    "    geom_point() +\n",
    "    geom_line() +\n",
    "    ggtitle(title) +\n",
    "    labs(x = \"年\", y =  label)\n",
    "}\n",
    "#LSTM(Long Short Term Memory)の前処理\n",
    "#時系列データts_dfをwindowで指定した長さ毎に区切って訓練用のデータを生成する。\n",
    "LSTM <- function(ts_df, window, rm.na = F) {\n",
    "  data.x = NULL\n",
    "  data.y = NULL\n",
    "  n = dim(ts_df)[2]\n",
    "  for(i in 1:n){\n",
    "    ts_x = ts_df[,i]\n",
    "    for(j in 1:(length(ts_x)-window)){\n",
    "      if(rm.na){\n",
    "        tmp.x = ts_x[1:window + j -1]\n",
    "        tmp.y = ts_x[1:window + j -1]\n",
    "        if(sum(c(is.na(tmp.x),is.na(tmp.y))) == 0){\n",
    "          data.x = rbind(data.x,ts_x[1:window + j -1])\n",
    "          data.y = rbind(data.y,ts_x[window + j])          \n",
    "        }\n",
    "      }else{\n",
    "        data.x = rbind(data.x,ts_x[1:window + j -1])\n",
    "        data.y = rbind(data.y,ts_x[window + j])\n",
    "      }\n",
    "    }\n",
    "  }\n",
    "  data <- list(x = array_reshape(data.x,c(dim(data.x),1)),\n",
    "               y = data.y)\n",
    "  scale = max(ts_df, na.rm = T)\n",
    "  x = data$x / scale\n",
    "  y = data$y / scale\n",
    "  lstm <- keras_model_sequential()\n",
    "  lstm %>%\n",
    "    layer_lstm(units = 64,input_shape = c(dim(x)[2],1)) %>%\n",
    "    layer_dropout(rate = 0.4) %>%\n",
    "    layer_dense(units = 1)\n",
    "  lstm %>% compile(loss = \"mean_squared_error\",\n",
    "                   optimizer = optimizer_adam(),\n",
    "                   metrics = \"accuracy\")\n",
    "  lstm %>% fit(x,y,\n",
    "               epochs = 1000,batch_size = 10, validation_split = 0.2)\n",
    "  i = 1\n",
    "  test_x = ts_df[,i]\n",
    "  test_x = data$x\n",
    "  scale=max(ts_df,na.rm = T)\n",
    "  test_x = test_x/scale\n",
    "  pred_x = lstm %>% \n",
    "    predict(test_x)  \n",
    "  ts.plot(ts_df[,i]/scale->a,ylim = c(min(c(a,pred_x)), max(c(a,pred_x))), ylab = \"検証値\")\n",
    "  lines(c(rep(NA,window),pred_x), col = 2)\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ローデータの目視確認\n",
    "- 読込\n",
    "- 要約統計量を求める。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
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   "outputs": [
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       "<caption>A data.frame: 29 × 13</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>id</th><th scope=col>time</th><th scope=col>Y1</th><th scope=col>Y2</th><th scope=col>Y3</th><th scope=col>Y4</th><th scope=col>Y5</th><th scope=col>Y6</th><th scope=col>Y7</th><th scope=col>Y8</th><th scope=col>Y9</th><th scope=col>Y10</th><th scope=col>Y11</th></tr>\n",
       "\t<tr><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>0</td><td>1986</td><td>11.36838</td><td>1111250</td><td>0</td><td>20.90</td><td>1.212152e-313</td><td> 42.4</td><td> 85.9</td><td>2.6</td><td>217.71013</td><td>58.594</td><td>0</td></tr>\n",
       "\t<tr><td>0</td><td>1987</td><td>11.91077</td><td>1111019</td><td>0</td><td>24.12</td><td>1.437787e-313</td><td> 41.9</td><td> 85.9</td><td>2.8</td><td>251.03609</td><td>58.594</td><td>0</td></tr>\n",
       "\t<tr><td>0</td><td>1988</td><td>12.58026</td><td>1186750</td><td>0</td><td>18.37</td><td>1.734345e-313</td><td> 40.4</td><td> 86.5</td><td>2.8</td><td>348.65896</td><td>58.594</td><td>0</td></tr>\n",
       "\t<tr><td>0</td><td>1989</td><td>13.02217</td><td>1216857</td><td>0</td><td>22.40</td><td>1.735299e-313</td><td> 38.7</td><td> 88.5</td><td>2.5</td><td>439.72734</td><td>58.594</td><td>0</td></tr>\n",
       "\t<tr><td>0</td><td>1990</td><td>13.54005</td><td>1304676</td><td>0</td><td>27.67</td><td>1.772752e-313</td><td> 36.8</td><td> 91.2</td><td>2.3</td><td>261.49901</td><td>53.711</td><td>0</td></tr>\n",
       "\t<tr><td>0</td><td>1991</td><td>13.73636</td><td>1318934</td><td>1</td><td>23.65</td><td>1.974477e-313</td><td> 36.2</td><td> 94.3</td><td>2.1</td><td>243.73033</td><td>53.711</td><td>0</td></tr>\n",
       "\t<tr><td>0</td><td>1992</td><td>13.85659</td><td>1330643</td><td>0</td><td>22.79</td><td>2.087208e-313</td><td> 36.9</td><td> 95.8</td><td>2.1</td><td>176.66962</td><td>43.640</td><td>1</td></tr>\n",
       "\t<tr><td>0</td><td>1993</td><td>14.00667</td><td>1333073</td><td>0</td><td>19.51</td><td>2.313130e-313</td><td> 39.9</td><td> 97.1</td><td>2.2</td><td>179.37425</td><td>43.640</td><td>0</td></tr>\n",
       "\t<tr><td>0</td><td>1994</td><td>14.50500</td><td>1385835</td><td>0</td><td>18.97</td><td>2.530688e-313</td><td> 41.1</td><td> 97.7</td><td>2.5</td><td>201.87369</td><td>43.640</td><td>1</td></tr>\n",
       "\t<tr><td>0</td><td>1995</td><td>14.96408</td><td>1421641</td><td>1</td><td>18.69</td><td>2.772627e-313</td><td> 43.6</td><td> 97.6</td><td>2.9</td><td>203.56711</td><td>43.640</td><td>0</td></tr>\n",
       "\t<tr><td>0</td><td>1996</td><td>15.20326</td><td>1424113</td><td>0</td><td>22.65</td><td>2.444392e-313</td><td> 46.3</td><td> 97.7</td><td>3.2</td><td>198.17144</td><td>47.600</td><td>0</td></tr>\n",
       "\t<tr><td>0</td><td>1997</td><td>15.31663</td><td>1414397</td><td>0</td><td>22.31</td><td>2.234803e-313</td><td> 48.4</td><td> 99.5</td><td>3.4</td><td>153.35417</td><td>47.600</td><td>1</td></tr>\n",
       "\t<tr><td>0</td><td>1998</td><td>15.11348</td><td>1371363</td><td>0</td><td>15.90</td><td>2.039227e-313</td><td> 56.1</td><td>100.1</td><td>3.4</td><td>138.28342</td><td>47.600</td><td>0</td></tr>\n",
       "\t<tr><td>0</td><td>1999</td><td>15.48801</td><td>1399009</td><td>1</td><td>22.42</td><td>2.265293e-313</td><td> 63.5</td><td> 99.8</td><td>4.1</td><td>189.72285</td><td>47.600</td><td>0</td></tr>\n",
       "\t<tr><td>0</td><td>2000</td><td>15.65519</td><td>1406195</td><td>0</td><td>35.48</td><td>2.412343e-313</td><td> 69.5</td><td> 99.1</td><td>4.7</td><td>139.10888</td><td>48.542</td><td>0</td></tr>\n",
       "\t<tr><td>0</td><td>2001</td><td>15.45515</td><td>1387952</td><td>1</td><td>31.80</td><td>2.130494e-313</td><td> 75.6</td><td> 98.4</td><td>4.7</td><td>107.14045</td><td>48.542</td><td>0</td></tr>\n",
       "\t<tr><td>0</td><td>2002</td><td>15.68053</td><td>1422453</td><td>0</td><td>32.94</td><td>2.051602e-313</td><td> 81.8</td><td> 97.5</td><td>5.0</td><td> 87.98923</td><td>48.542</td><td>0</td></tr>\n",
       "\t<tr><td>1</td><td>2003</td><td>15.53167</td><td>1428102</td><td>0</td><td>36.30</td><td>2.232162e-313</td><td> 88.2</td><td> 97.2</td><td>5.4</td><td>109.84198</td><td>49.375</td><td>0</td></tr>\n",
       "\t<tr><td>0</td><td>2004</td><td>15.73810</td><td>1427399</td><td>1</td><td>44.38</td><td>2.440000e-313</td><td> 95.8</td><td> 97.2</td><td>5.3</td><td>118.19712</td><td>49.375</td><td>0</td></tr>\n",
       "\t<tr><td>0</td><td>2005</td><td>15.67054</td><td>1439905</td><td>0</td><td>60.88</td><td>2.417166e-313</td><td>100.2</td><td> 97.0</td><td>4.7</td><td>166.26863</td><td>61.667</td><td>1</td></tr>\n",
       "\t<tr><td>0</td><td>2006</td><td>15.71379</td><td>1425899</td><td>0</td><td>71.49</td><td>2.302782e-313</td><td>100.5</td><td> 97.2</td><td>4.4</td><td>177.22047</td><td>61.667</td><td>0</td></tr>\n",
       "\t<tr><td>1</td><td>2007</td><td>15.44623</td><td>1461356</td><td>1</td><td>74.52</td><td>2.302205e-313</td><td>102.0</td><td> 97.2</td><td>4.1</td><td>157.48745</td><td>61.667</td><td>0</td></tr>\n",
       "\t<tr><td>0</td><td>2008</td><td>14.35917</td><td>1385165</td><td>0</td><td>94.32</td><td>2.560759e-313</td><td>107.2</td><td> 98.6</td><td>3.9</td><td> 89.85355</td><td>61.667</td><td>1</td></tr>\n",
       "\t<tr><td>0</td><td>2009</td><td>14.08942</td><td>1290244</td><td>0</td><td>64.02</td><td>2.659101e-313</td><td>120.7</td><td> 97.2</td><td>4.0</td><td>108.50247</td><td>24.792</td><td>0</td></tr>\n",
       "\t<tr><td>1</td><td>2010</td><td>14.69798</td><td>1350428</td><td>0</td><td>79.04</td><td>2.856209e-313</td><td>127.5</td><td> 96.5</td><td>5.1</td><td>105.99917</td><td>24.792</td><td>1</td></tr>\n",
       "\t<tr><td>0</td><td>2011</td><td>14.30011</td><td>1396767</td><td>0</td><td>93.72</td><td>3.129825e-313</td><td>135.6</td><td> 96.3</td><td>5.1</td><td> 87.80218</td><td>24.792</td><td>0</td></tr>\n",
       "\t<tr><td>0</td><td>2012</td><td>14.11543</td><td>1478859</td><td>0</td><td>95.30</td><td>3.175953e-313</td><td>142.5</td><td> 96.2</td><td>4.6</td><td>108.05800</td><td>61.250</td><td>1</td></tr>\n",
       "\t<tr><td>0</td><td>2013</td><td>14.00908</td><td>1315869</td><td>0</td><td>94.86</td><td>2.645133e-313</td><td>146.6</td><td> 96.6</td><td>4.3</td><td>168.64710</td><td>61.250</td><td>1</td></tr>\n",
       "\t<tr><td>1</td><td>2014</td><td>13.55841</td><td>1268712</td><td>0</td><td>89.09</td><td>2.490221e-313</td><td>149.5</td><td> 99.2</td><td>4.0</td><td>175.91502</td><td>61.263</td><td>0</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 29 × 13\n",
       "\\begin{tabular}{lllllllllllll}\n",
       " id & time & Y1 & Y2 & Y3 & Y4 & Y5 & Y6 & Y7 & Y8 & Y9 & Y10 & Y11\\\\\n",
       " <int> & <int> & <dbl> & <int> & <int> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <int>\\\\\n",
       "\\hline\n",
       "\t 0 & 1986 & 11.36838 & 1111250 & 0 & 20.90 & 1.212152e-313 &  42.4 &  85.9 & 2.6 & 217.71013 & 58.594 & 0\\\\\n",
       "\t 0 & 1987 & 11.91077 & 1111019 & 0 & 24.12 & 1.437787e-313 &  41.9 &  85.9 & 2.8 & 251.03609 & 58.594 & 0\\\\\n",
       "\t 0 & 1988 & 12.58026 & 1186750 & 0 & 18.37 & 1.734345e-313 &  40.4 &  86.5 & 2.8 & 348.65896 & 58.594 & 0\\\\\n",
       "\t 0 & 1989 & 13.02217 & 1216857 & 0 & 22.40 & 1.735299e-313 &  38.7 &  88.5 & 2.5 & 439.72734 & 58.594 & 0\\\\\n",
       "\t 0 & 1990 & 13.54005 & 1304676 & 0 & 27.67 & 1.772752e-313 &  36.8 &  91.2 & 2.3 & 261.49901 & 53.711 & 0\\\\\n",
       "\t 0 & 1991 & 13.73636 & 1318934 & 1 & 23.65 & 1.974477e-313 &  36.2 &  94.3 & 2.1 & 243.73033 & 53.711 & 0\\\\\n",
       "\t 0 & 1992 & 13.85659 & 1330643 & 0 & 22.79 & 2.087208e-313 &  36.9 &  95.8 & 2.1 & 176.66962 & 43.640 & 1\\\\\n",
       "\t 0 & 1993 & 14.00667 & 1333073 & 0 & 19.51 & 2.313130e-313 &  39.9 &  97.1 & 2.2 & 179.37425 & 43.640 & 0\\\\\n",
       "\t 0 & 1994 & 14.50500 & 1385835 & 0 & 18.97 & 2.530688e-313 &  41.1 &  97.7 & 2.5 & 201.87369 & 43.640 & 1\\\\\n",
       "\t 0 & 1995 & 14.96408 & 1421641 & 1 & 18.69 & 2.772627e-313 &  43.6 &  97.6 & 2.9 & 203.56711 & 43.640 & 0\\\\\n",
       "\t 0 & 1996 & 15.20326 & 1424113 & 0 & 22.65 & 2.444392e-313 &  46.3 &  97.7 & 3.2 & 198.17144 & 47.600 & 0\\\\\n",
       "\t 0 & 1997 & 15.31663 & 1414397 & 0 & 22.31 & 2.234803e-313 &  48.4 &  99.5 & 3.4 & 153.35417 & 47.600 & 1\\\\\n",
       "\t 0 & 1998 & 15.11348 & 1371363 & 0 & 15.90 & 2.039227e-313 &  56.1 & 100.1 & 3.4 & 138.28342 & 47.600 & 0\\\\\n",
       "\t 0 & 1999 & 15.48801 & 1399009 & 1 & 22.42 & 2.265293e-313 &  63.5 &  99.8 & 4.1 & 189.72285 & 47.600 & 0\\\\\n",
       "\t 0 & 2000 & 15.65519 & 1406195 & 0 & 35.48 & 2.412343e-313 &  69.5 &  99.1 & 4.7 & 139.10888 & 48.542 & 0\\\\\n",
       "\t 0 & 2001 & 15.45515 & 1387952 & 1 & 31.80 & 2.130494e-313 &  75.6 &  98.4 & 4.7 & 107.14045 & 48.542 & 0\\\\\n",
       "\t 0 & 2002 & 15.68053 & 1422453 & 0 & 32.94 & 2.051602e-313 &  81.8 &  97.5 & 5.0 &  87.98923 & 48.542 & 0\\\\\n",
       "\t 1 & 2003 & 15.53167 & 1428102 & 0 & 36.30 & 2.232162e-313 &  88.2 &  97.2 & 5.4 & 109.84198 & 49.375 & 0\\\\\n",
       "\t 0 & 2004 & 15.73810 & 1427399 & 1 & 44.38 & 2.440000e-313 &  95.8 &  97.2 & 5.3 & 118.19712 & 49.375 & 0\\\\\n",
       "\t 0 & 2005 & 15.67054 & 1439905 & 0 & 60.88 & 2.417166e-313 & 100.2 &  97.0 & 4.7 & 166.26863 & 61.667 & 1\\\\\n",
       "\t 0 & 2006 & 15.71379 & 1425899 & 0 & 71.49 & 2.302782e-313 & 100.5 &  97.2 & 4.4 & 177.22047 & 61.667 & 0\\\\\n",
       "\t 1 & 2007 & 15.44623 & 1461356 & 1 & 74.52 & 2.302205e-313 & 102.0 &  97.2 & 4.1 & 157.48745 & 61.667 & 0\\\\\n",
       "\t 0 & 2008 & 14.35917 & 1385165 & 0 & 94.32 & 2.560759e-313 & 107.2 &  98.6 & 3.9 &  89.85355 & 61.667 & 1\\\\\n",
       "\t 0 & 2009 & 14.08942 & 1290244 & 0 & 64.02 & 2.659101e-313 & 120.7 &  97.2 & 4.0 & 108.50247 & 24.792 & 0\\\\\n",
       "\t 1 & 2010 & 14.69798 & 1350428 & 0 & 79.04 & 2.856209e-313 & 127.5 &  96.5 & 5.1 & 105.99917 & 24.792 & 1\\\\\n",
       "\t 0 & 2011 & 14.30011 & 1396767 & 0 & 93.72 & 3.129825e-313 & 135.6 &  96.3 & 5.1 &  87.80218 & 24.792 & 0\\\\\n",
       "\t 0 & 2012 & 14.11543 & 1478859 & 0 & 95.30 & 3.175953e-313 & 142.5 &  96.2 & 4.6 & 108.05800 & 61.250 & 1\\\\\n",
       "\t 0 & 2013 & 14.00908 & 1315869 & 0 & 94.86 & 2.645133e-313 & 146.6 &  96.6 & 4.3 & 168.64710 & 61.250 & 1\\\\\n",
       "\t 1 & 2014 & 13.55841 & 1268712 & 0 & 89.09 & 2.490221e-313 & 149.5 &  99.2 & 4.0 & 175.91502 & 61.263 & 0\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 29 × 13\n",
       "\n",
       "| id &lt;int&gt; | time &lt;int&gt; | Y1 &lt;dbl&gt; | Y2 &lt;int&gt; | Y3 &lt;int&gt; | Y4 &lt;dbl&gt; | Y5 &lt;dbl&gt; | Y6 &lt;dbl&gt; | Y7 &lt;dbl&gt; | Y8 &lt;dbl&gt; | Y9 &lt;dbl&gt; | Y10 &lt;dbl&gt; | Y11 &lt;int&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 0 | 1986 | 11.36838 | 1111250 | 0 | 20.90 | 1.212152e-313 |  42.4 |  85.9 | 2.6 | 217.71013 | 58.594 | 0 |\n",
       "| 0 | 1987 | 11.91077 | 1111019 | 0 | 24.12 | 1.437787e-313 |  41.9 |  85.9 | 2.8 | 251.03609 | 58.594 | 0 |\n",
       "| 0 | 1988 | 12.58026 | 1186750 | 0 | 18.37 | 1.734345e-313 |  40.4 |  86.5 | 2.8 | 348.65896 | 58.594 | 0 |\n",
       "| 0 | 1989 | 13.02217 | 1216857 | 0 | 22.40 | 1.735299e-313 |  38.7 |  88.5 | 2.5 | 439.72734 | 58.594 | 0 |\n",
       "| 0 | 1990 | 13.54005 | 1304676 | 0 | 27.67 | 1.772752e-313 |  36.8 |  91.2 | 2.3 | 261.49901 | 53.711 | 0 |\n",
       "| 0 | 1991 | 13.73636 | 1318934 | 1 | 23.65 | 1.974477e-313 |  36.2 |  94.3 | 2.1 | 243.73033 | 53.711 | 0 |\n",
       "| 0 | 1992 | 13.85659 | 1330643 | 0 | 22.79 | 2.087208e-313 |  36.9 |  95.8 | 2.1 | 176.66962 | 43.640 | 1 |\n",
       "| 0 | 1993 | 14.00667 | 1333073 | 0 | 19.51 | 2.313130e-313 |  39.9 |  97.1 | 2.2 | 179.37425 | 43.640 | 0 |\n",
       "| 0 | 1994 | 14.50500 | 1385835 | 0 | 18.97 | 2.530688e-313 |  41.1 |  97.7 | 2.5 | 201.87369 | 43.640 | 1 |\n",
       "| 0 | 1995 | 14.96408 | 1421641 | 1 | 18.69 | 2.772627e-313 |  43.6 |  97.6 | 2.9 | 203.56711 | 43.640 | 0 |\n",
       "| 0 | 1996 | 15.20326 | 1424113 | 0 | 22.65 | 2.444392e-313 |  46.3 |  97.7 | 3.2 | 198.17144 | 47.600 | 0 |\n",
       "| 0 | 1997 | 15.31663 | 1414397 | 0 | 22.31 | 2.234803e-313 |  48.4 |  99.5 | 3.4 | 153.35417 | 47.600 | 1 |\n",
       "| 0 | 1998 | 15.11348 | 1371363 | 0 | 15.90 | 2.039227e-313 |  56.1 | 100.1 | 3.4 | 138.28342 | 47.600 | 0 |\n",
       "| 0 | 1999 | 15.48801 | 1399009 | 1 | 22.42 | 2.265293e-313 |  63.5 |  99.8 | 4.1 | 189.72285 | 47.600 | 0 |\n",
       "| 0 | 2000 | 15.65519 | 1406195 | 0 | 35.48 | 2.412343e-313 |  69.5 |  99.1 | 4.7 | 139.10888 | 48.542 | 0 |\n",
       "| 0 | 2001 | 15.45515 | 1387952 | 1 | 31.80 | 2.130494e-313 |  75.6 |  98.4 | 4.7 | 107.14045 | 48.542 | 0 |\n",
       "| 0 | 2002 | 15.68053 | 1422453 | 0 | 32.94 | 2.051602e-313 |  81.8 |  97.5 | 5.0 |  87.98923 | 48.542 | 0 |\n",
       "| 1 | 2003 | 15.53167 | 1428102 | 0 | 36.30 | 2.232162e-313 |  88.2 |  97.2 | 5.4 | 109.84198 | 49.375 | 0 |\n",
       "| 0 | 2004 | 15.73810 | 1427399 | 1 | 44.38 | 2.440000e-313 |  95.8 |  97.2 | 5.3 | 118.19712 | 49.375 | 0 |\n",
       "| 0 | 2005 | 15.67054 | 1439905 | 0 | 60.88 | 2.417166e-313 | 100.2 |  97.0 | 4.7 | 166.26863 | 61.667 | 1 |\n",
       "| 0 | 2006 | 15.71379 | 1425899 | 0 | 71.49 | 2.302782e-313 | 100.5 |  97.2 | 4.4 | 177.22047 | 61.667 | 0 |\n",
       "| 1 | 2007 | 15.44623 | 1461356 | 1 | 74.52 | 2.302205e-313 | 102.0 |  97.2 | 4.1 | 157.48745 | 61.667 | 0 |\n",
       "| 0 | 2008 | 14.35917 | 1385165 | 0 | 94.32 | 2.560759e-313 | 107.2 |  98.6 | 3.9 |  89.85355 | 61.667 | 1 |\n",
       "| 0 | 2009 | 14.08942 | 1290244 | 0 | 64.02 | 2.659101e-313 | 120.7 |  97.2 | 4.0 | 108.50247 | 24.792 | 0 |\n",
       "| 1 | 2010 | 14.69798 | 1350428 | 0 | 79.04 | 2.856209e-313 | 127.5 |  96.5 | 5.1 | 105.99917 | 24.792 | 1 |\n",
       "| 0 | 2011 | 14.30011 | 1396767 | 0 | 93.72 | 3.129825e-313 | 135.6 |  96.3 | 5.1 |  87.80218 | 24.792 | 0 |\n",
       "| 0 | 2012 | 14.11543 | 1478859 | 0 | 95.30 | 3.175953e-313 | 142.5 |  96.2 | 4.6 | 108.05800 | 61.250 | 1 |\n",
       "| 0 | 2013 | 14.00908 | 1315869 | 0 | 94.86 | 2.645133e-313 | 146.6 |  96.6 | 4.3 | 168.64710 | 61.250 | 1 |\n",
       "| 1 | 2014 | 13.55841 | 1268712 | 0 | 89.09 | 2.490221e-313 | 149.5 |  99.2 | 4.0 | 175.91502 | 61.263 | 0 |\n",
       "\n"
      ],
      "text/plain": [
       "   id time Y1       Y2      Y3 Y4    Y5            Y6    Y7    Y8  Y9       \n",
       "1  0  1986 11.36838 1111250 0  20.90 1.212152e-313  42.4  85.9 2.6 217.71013\n",
       "2  0  1987 11.91077 1111019 0  24.12 1.437787e-313  41.9  85.9 2.8 251.03609\n",
       "3  0  1988 12.58026 1186750 0  18.37 1.734345e-313  40.4  86.5 2.8 348.65896\n",
       "4  0  1989 13.02217 1216857 0  22.40 1.735299e-313  38.7  88.5 2.5 439.72734\n",
       "5  0  1990 13.54005 1304676 0  27.67 1.772752e-313  36.8  91.2 2.3 261.49901\n",
       "6  0  1991 13.73636 1318934 1  23.65 1.974477e-313  36.2  94.3 2.1 243.73033\n",
       "7  0  1992 13.85659 1330643 0  22.79 2.087208e-313  36.9  95.8 2.1 176.66962\n",
       "8  0  1993 14.00667 1333073 0  19.51 2.313130e-313  39.9  97.1 2.2 179.37425\n",
       "9  0  1994 14.50500 1385835 0  18.97 2.530688e-313  41.1  97.7 2.5 201.87369\n",
       "10 0  1995 14.96408 1421641 1  18.69 2.772627e-313  43.6  97.6 2.9 203.56711\n",
       "11 0  1996 15.20326 1424113 0  22.65 2.444392e-313  46.3  97.7 3.2 198.17144\n",
       "12 0  1997 15.31663 1414397 0  22.31 2.234803e-313  48.4  99.5 3.4 153.35417\n",
       "13 0  1998 15.11348 1371363 0  15.90 2.039227e-313  56.1 100.1 3.4 138.28342\n",
       "14 0  1999 15.48801 1399009 1  22.42 2.265293e-313  63.5  99.8 4.1 189.72285\n",
       "15 0  2000 15.65519 1406195 0  35.48 2.412343e-313  69.5  99.1 4.7 139.10888\n",
       "16 0  2001 15.45515 1387952 1  31.80 2.130494e-313  75.6  98.4 4.7 107.14045\n",
       "17 0  2002 15.68053 1422453 0  32.94 2.051602e-313  81.8  97.5 5.0  87.98923\n",
       "18 1  2003 15.53167 1428102 0  36.30 2.232162e-313  88.2  97.2 5.4 109.84198\n",
       "19 0  2004 15.73810 1427399 1  44.38 2.440000e-313  95.8  97.2 5.3 118.19712\n",
       "20 0  2005 15.67054 1439905 0  60.88 2.417166e-313 100.2  97.0 4.7 166.26863\n",
       "21 0  2006 15.71379 1425899 0  71.49 2.302782e-313 100.5  97.2 4.4 177.22047\n",
       "22 1  2007 15.44623 1461356 1  74.52 2.302205e-313 102.0  97.2 4.1 157.48745\n",
       "23 0  2008 14.35917 1385165 0  94.32 2.560759e-313 107.2  98.6 3.9  89.85355\n",
       "24 0  2009 14.08942 1290244 0  64.02 2.659101e-313 120.7  97.2 4.0 108.50247\n",
       "25 1  2010 14.69798 1350428 0  79.04 2.856209e-313 127.5  96.5 5.1 105.99917\n",
       "26 0  2011 14.30011 1396767 0  93.72 3.129825e-313 135.6  96.3 5.1  87.80218\n",
       "27 0  2012 14.11543 1478859 0  95.30 3.175953e-313 142.5  96.2 4.6 108.05800\n",
       "28 0  2013 14.00908 1315869 0  94.86 2.645133e-313 146.6  96.6 4.3 168.64710\n",
       "29 1  2014 13.55841 1268712 0  89.09 2.490221e-313 149.5  99.2 4.0 175.91502\n",
       "   Y10    Y11\n",
       "1  58.594 0  \n",
       "2  58.594 0  \n",
       "3  58.594 0  \n",
       "4  58.594 0  \n",
       "5  53.711 0  \n",
       "6  53.711 0  \n",
       "7  43.640 1  \n",
       "8  43.640 0  \n",
       "9  43.640 1  \n",
       "10 43.640 0  \n",
       "11 47.600 0  \n",
       "12 47.600 1  \n",
       "13 47.600 0  \n",
       "14 47.600 0  \n",
       "15 48.542 0  \n",
       "16 48.542 0  \n",
       "17 48.542 0  \n",
       "18 49.375 0  \n",
       "19 49.375 0  \n",
       "20 61.667 1  \n",
       "21 61.667 0  \n",
       "22 61.667 0  \n",
       "23 61.667 1  \n",
       "24 24.792 0  \n",
       "25 24.792 1  \n",
       "26 24.792 0  \n",
       "27 61.250 1  \n",
       "28 61.250 1  \n",
       "29 61.263 0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#読込\n",
    "raw_data <- fread(\"./0_input/raw_data.csv\") %>%\n",
    "  as.data.frame\n",
    "raw_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "       id              time            Y1              Y2         \n",
       " Min.   :0.0000   Min.   :1986   Min.   :11.37   Min.   :1111019  \n",
       " 1st Qu.:0.0000   1st Qu.:1993   1st Qu.:13.86   1st Qu.:1315869  \n",
       " Median :0.0000   Median :2000   Median :14.51   Median :1385835  \n",
       " Mean   :0.1379   Mean   :2000   Mean   :14.44   Mean   :1352237  \n",
       " 3rd Qu.:0.0000   3rd Qu.:2007   3rd Qu.:15.46   3rd Qu.:1422453  \n",
       " Max.   :1.0000   Max.   :2014   Max.   :15.74   Max.   :1478859  \n",
       "       Y3               Y4              Y5                   Y6        \n",
       " Min.   :0.0000   Min.   :15.90   Min.   :1.212e-313   Min.   : 36.20  \n",
       " 1st Qu.:0.0000   1st Qu.:22.40   1st Qu.:2.052e-313   1st Qu.: 41.90  \n",
       " Median :0.0000   Median :31.80   Median :2.303e-313   Median : 69.50  \n",
       " Mean   :0.2069   Mean   :44.78   Mean   :2.288e-313   Mean   : 77.77  \n",
       " 3rd Qu.:0.0000   3rd Qu.:71.49   3rd Qu.:2.531e-313   3rd Qu.:102.00  \n",
       " Max.   :1.0000   Max.   :95.30   Max.   :3.176e-313   Max.   :149.50  \n",
       "       Y7               Y8              Y9             Y10       \n",
       " Min.   : 85.90   Min.   :2.100   Min.   : 87.8   Min.   :24.79  \n",
       " 1st Qu.: 96.20   1st Qu.:2.800   1st Qu.:109.8   1st Qu.:47.60  \n",
       " Median : 97.20   Median :4.000   Median :168.6   Median :49.38  \n",
       " Mean   : 95.83   Mean   :3.731   Mean   :176.3   Mean   :50.20  \n",
       " 3rd Qu.: 97.70   3rd Qu.:4.700   3rd Qu.:201.9   3rd Qu.:58.59  \n",
       " Max.   :100.10   Max.   :5.400   Max.   :439.7   Max.   :61.67  \n",
       "      Y11        \n",
       " Min.   :0.0000  \n",
       " 1st Qu.:0.0000  \n",
       " Median :0.0000  \n",
       " Mean   :0.2759  \n",
       " 3rd Qu.:1.0000  \n",
       " Max.   :1.0000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 要約統計量を求める。\n",
    "raw_data %>%\n",
    "  summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 誤差項調整\n",
    "- 対数差分系列(一次のテイラー展開近似によって変化率に近似するとともに、定常状態として扱う為)\n",
    "- プロビット写像(Y3: 原子力事故・異常事象(有無)及びY11: 気候変動対策に関する合意(有無))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "New names:\n",
      "* `` -> ...1\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A matrix: 28 × 13 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>id</th><th scope=col>time</th><th scope=col>Y1</th><th scope=col>Y2</th><th scope=col>Y3</th><th scope=col>Y4</th><th scope=col>Y5</th><th scope=col>Y6</th><th scope=col>Y7</th><th scope=col>Y8</th><th scope=col>Y9</th><th scope=col>Y10</th><th scope=col>Y11</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>0</td><td>1987</td><td> 0.046607215</td><td>-0.0002078956</td><td> 0.0</td><td> 0.14329221</td><td> 0.1707078336</td><td>-0.011862535</td><td> 0.000000000</td><td> 0.07410797</td><td> 0.142432215</td><td> 0.0000000000</td><td> 0.0</td></tr>\n",
       "\t<tr><td>0</td><td>1988</td><td> 0.054685886</td><td> 0.0659408662</td><td> 0.0</td><td>-0.27232247</td><td> 0.1875246945</td><td>-0.036456042</td><td> 0.006960585</td><td> 0.00000000</td><td> 0.328497539</td><td> 0.0000000000</td><td> 0.0</td></tr>\n",
       "\t<tr><td>0</td><td>1989</td><td> 0.034524371</td><td> 0.0250528266</td><td> 0.0</td><td> 0.19834206</td><td> 0.0005499123</td><td>-0.042990185</td><td> 0.022858138</td><td>-0.11332869</td><td> 0.232060601</td><td> 0.0000000000</td><td> 0.0</td></tr>\n",
       "\t<tr><td>0</td><td>1990</td><td> 0.038998671</td><td> 0.0696834290</td><td> 0.0</td><td> 0.21128783</td><td> 0.0213534086</td><td>-0.050341755</td><td> 0.030052345</td><td>-0.08338161</td><td>-0.519724355</td><td>-0.0870144800</td><td> 0.0</td></tr>\n",
       "\t<tr><td>0</td><td>1991</td><td> 0.014394372</td><td> 0.0108691005</td><td>-0.5</td><td>-0.15698568</td><td> 0.1077704117</td><td>-0.016438726</td><td> 0.033426293</td><td>-0.09097178</td><td>-0.070368088</td><td> 0.0000000000</td><td> 0.0</td></tr>\n",
       "\t<tr><td>0</td><td>1992</td><td> 0.008714600</td><td> 0.0088384495</td><td> 0.5</td><td>-0.03704127</td><td> 0.0555237346</td><td> 0.019152432</td><td> 0.015781495</td><td> 0.00000000</td><td>-0.321780974</td><td>-0.2076436613</td><td>-0.5</td></tr>\n",
       "\t<tr><td>0</td><td>1993</td><td> 0.010772713</td><td> 0.0018245193</td><td> 0.0</td><td>-0.15539469</td><td> 0.1027742980</td><td> 0.078164773</td><td> 0.013478690</td><td> 0.04652002</td><td> 0.015192971</td><td> 0.0000000000</td><td> 0.5</td></tr>\n",
       "\t<tr><td>0</td><td>1994</td><td> 0.034959773</td><td> 0.0388160427</td><td> 0.0</td><td>-0.02806837</td><td> 0.0898896168</td><td> 0.029631798</td><td> 0.006160184</td><td> 0.12783337</td><td> 0.118167799</td><td> 0.0000000000</td><td>-0.5</td></tr>\n",
       "\t<tr><td>0</td><td>1995</td><td> 0.031159245</td><td> 0.0255089921</td><td>-0.5</td><td>-0.01487016</td><td> 0.0913040437</td><td> 0.059049029</td><td>-0.001024066</td><td> 0.14842001</td><td> 0.008353525</td><td> 0.0000000000</td><td> 0.5</td></tr>\n",
       "\t<tr><td>0</td><td>1996</td><td> 0.015857216</td><td> 0.0017373256</td><td> 0.5</td><td> 0.19217123</td><td>-0.1259988250</td><td> 0.060084811</td><td> 0.001024066</td><td> 0.09844007</td><td>-0.026863215</td><td> 0.0868586003</td><td> 0.0</td></tr>\n",
       "\t<tr><td>0</td><td>1997</td><td> 0.007429288</td><td>-0.0068458719</td><td> 0.0</td><td>-0.01512484</td><td>-0.0896433402</td><td> 0.044357853</td><td> 0.018256085</td><td> 0.06062462</td><td>-0.256382432</td><td> 0.0000000000</td><td>-0.5</td></tr>\n",
       "\t<tr><td>0</td><td>1998</td><td>-0.013352105</td><td>-0.0308981561</td><td> 0.0</td><td>-0.33871590</td><td>-0.0915822665</td><td> 0.147635999</td><td> 0.006012042</td><td> 0.00000000</td><td>-0.103444736</td><td> 0.0000000000</td><td> 0.5</td></tr>\n",
       "\t<tr><td>0</td><td>1999</td><td> 0.024479115</td><td> 0.0199589931</td><td>-0.5</td><td> 0.34363431</td><td> 0.1051332959</td><td> 0.123904093</td><td>-0.003001503</td><td> 0.18721154</td><td> 0.316258976</td><td> 0.0000000000</td><td> 0.0</td></tr>\n",
       "\t<tr><td>0</td><td>2000</td><td> 0.010736315</td><td> 0.0051233463</td><td> 0.5</td><td> 0.45901574</td><td> 0.0628943640</td><td> 0.090286847</td><td>-0.007038742</td><td> 0.13657554</td><td>-0.310307387</td><td> 0.0195966413</td><td> 0.0</td></tr>\n",
       "\t<tr><td>0</td><td>2001</td><td>-0.012860210</td><td>-0.0130581957</td><td>-0.5</td><td>-0.10950287</td><td>-0.1242445967</td><td> 0.084129531</td><td>-0.007088637</td><td> 0.00000000</td><td>-0.261116345</td><td> 0.0000000000</td><td> 0.0</td></tr>\n",
       "\t<tr><td>0</td><td>2002</td><td> 0.014477534</td><td> 0.0245535667</td><td> 0.5</td><td> 0.03522143</td><td>-0.0377329263</td><td> 0.078820960</td><td>-0.009188426</td><td> 0.06187540</td><td>-0.196926170</td><td> 0.0000000000</td><td> 0.0</td></tr>\n",
       "\t<tr><td>1</td><td>2003</td><td>-0.009538650</td><td> 0.0039634439</td><td> 0.0</td><td> 0.09713002</td><td> 0.0843496712</td><td> 0.075329719</td><td>-0.003081667</td><td> 0.07696104</td><td> 0.221828367</td><td> 0.0170148206</td><td> 0.0</td></tr>\n",
       "\t<tr><td>0</td><td>2004</td><td> 0.013203359</td><td>-0.0004923830</td><td>-0.5</td><td> 0.20097118</td><td> 0.0890274168</td><td> 0.082655722</td><td> 0.000000000</td><td>-0.01869213</td><td> 0.073310952</td><td> 0.0000000000</td><td> 0.0</td></tr>\n",
       "\t<tr><td>0</td><td>2005</td><td>-0.004302008</td><td> 0.0087232322</td><td> 0.5</td><td> 0.31611580</td><td>-0.0094022598</td><td> 0.044905504</td><td>-0.002059733</td><td>-0.12014431</td><td> 0.341250994</td><td> 0.2223047186</td><td>-0.5</td></tr>\n",
       "\t<tr><td>0</td><td>2006</td><td> 0.002756154</td><td>-0.0097746472</td><td> 0.0</td><td> 0.16065287</td><td>-0.0484778223</td><td> 0.002989539</td><td> 0.002059733</td><td>-0.06595797</td><td> 0.063789817</td><td> 0.0000000000</td><td> 0.5</td></tr>\n",
       "\t<tr><td>1</td><td>2007</td><td>-0.017173710</td><td> 0.0245622798</td><td>-0.5</td><td> 0.04150997</td><td>-0.0002505979</td><td> 0.014815086</td><td> 0.000000000</td><td>-0.07061757</td><td>-0.118048778</td><td> 0.0000000000</td><td> 0.0</td></tr>\n",
       "\t<tr><td>0</td><td>2008</td><td>-0.072976198</td><td>-0.0535455057</td><td> 0.5</td><td> 0.23562571</td><td> 0.1064363396</td><td> 0.049723435</td><td> 0.014300550</td><td>-0.05001042</td><td>-0.561164650</td><td> 0.0000000000</td><td>-0.5</td></tr>\n",
       "\t<tr><td>0</td><td>2009</td><td>-0.018964601</td><td>-0.0709879183</td><td> 0.0</td><td>-0.38749772</td><td> 0.0376843968</td><td> 0.118611879</td><td>-0.014300550</td><td> 0.02531781</td><td> 0.188591815</td><td>-0.9112279213</td><td> 0.5</td></tr>\n",
       "\t<tr><td>1</td><td>2010</td><td> 0.042285908</td><td> 0.0455902315</td><td> 0.0</td><td> 0.21075852</td><td> 0.0715071253</td><td> 0.054808236</td><td>-0.007227703</td><td> 0.24294618</td><td>-0.023341674</td><td> 0.0000000000</td><td>-0.5</td></tr>\n",
       "\t<tr><td>0</td><td>2011</td><td>-0.027442840</td><td> 0.0337387012</td><td> 0.0</td><td> 0.17035756</td><td> 0.0914818714</td><td> 0.061593011</td><td>-0.002074690</td><td> 0.00000000</td><td>-0.188344934</td><td> 0.0000000000</td><td> 0.5</td></tr>\n",
       "\t<tr><td>0</td><td>2012</td><td>-0.012998704</td><td> 0.0571105641</td><td> 0.0</td><td> 0.01671820</td><td> 0.0146306523</td><td> 0.049632624</td><td>-0.001038961</td><td>-0.10318424</td><td> 0.207581791</td><td> 0.9044428289</td><td>-0.5</td></tr>\n",
       "\t<tr><td>0</td><td>2013</td><td>-0.007562834</td><td>-0.1167735606</td><td> 0.0</td><td>-0.00462769</td><td>-0.1828863972</td><td> 0.028365790</td><td> 0.004149384</td><td>-0.06744128</td><td> 0.445140246</td><td> 0.0000000000</td><td> 0.0</td></tr>\n",
       "\t<tr><td>1</td><td>2014</td><td>-0.032698672</td><td>-0.0364950713</td><td> 0.0</td><td>-0.06275503</td><td>-0.0603498861</td><td> 0.019588603</td><td> 0.026559273</td><td>-0.07232066</td><td> 0.042192672</td><td> 0.0002122224</td><td> 0.5</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 28 × 13 of type dbl\n",
       "\\begin{tabular}{lllllllllllll}\n",
       " id & time & Y1 & Y2 & Y3 & Y4 & Y5 & Y6 & Y7 & Y8 & Y9 & Y10 & Y11\\\\\n",
       "\\hline\n",
       "\t 0 & 1987 &  0.046607215 & -0.0002078956 &  0.0 &  0.14329221 &  0.1707078336 & -0.011862535 &  0.000000000 &  0.07410797 &  0.142432215 &  0.0000000000 &  0.0\\\\\n",
       "\t 0 & 1988 &  0.054685886 &  0.0659408662 &  0.0 & -0.27232247 &  0.1875246945 & -0.036456042 &  0.006960585 &  0.00000000 &  0.328497539 &  0.0000000000 &  0.0\\\\\n",
       "\t 0 & 1989 &  0.034524371 &  0.0250528266 &  0.0 &  0.19834206 &  0.0005499123 & -0.042990185 &  0.022858138 & -0.11332869 &  0.232060601 &  0.0000000000 &  0.0\\\\\n",
       "\t 0 & 1990 &  0.038998671 &  0.0696834290 &  0.0 &  0.21128783 &  0.0213534086 & -0.050341755 &  0.030052345 & -0.08338161 & -0.519724355 & -0.0870144800 &  0.0\\\\\n",
       "\t 0 & 1991 &  0.014394372 &  0.0108691005 & -0.5 & -0.15698568 &  0.1077704117 & -0.016438726 &  0.033426293 & -0.09097178 & -0.070368088 &  0.0000000000 &  0.0\\\\\n",
       "\t 0 & 1992 &  0.008714600 &  0.0088384495 &  0.5 & -0.03704127 &  0.0555237346 &  0.019152432 &  0.015781495 &  0.00000000 & -0.321780974 & -0.2076436613 & -0.5\\\\\n",
       "\t 0 & 1993 &  0.010772713 &  0.0018245193 &  0.0 & -0.15539469 &  0.1027742980 &  0.078164773 &  0.013478690 &  0.04652002 &  0.015192971 &  0.0000000000 &  0.5\\\\\n",
       "\t 0 & 1994 &  0.034959773 &  0.0388160427 &  0.0 & -0.02806837 &  0.0898896168 &  0.029631798 &  0.006160184 &  0.12783337 &  0.118167799 &  0.0000000000 & -0.5\\\\\n",
       "\t 0 & 1995 &  0.031159245 &  0.0255089921 & -0.5 & -0.01487016 &  0.0913040437 &  0.059049029 & -0.001024066 &  0.14842001 &  0.008353525 &  0.0000000000 &  0.5\\\\\n",
       "\t 0 & 1996 &  0.015857216 &  0.0017373256 &  0.5 &  0.19217123 & -0.1259988250 &  0.060084811 &  0.001024066 &  0.09844007 & -0.026863215 &  0.0868586003 &  0.0\\\\\n",
       "\t 0 & 1997 &  0.007429288 & -0.0068458719 &  0.0 & -0.01512484 & -0.0896433402 &  0.044357853 &  0.018256085 &  0.06062462 & -0.256382432 &  0.0000000000 & -0.5\\\\\n",
       "\t 0 & 1998 & -0.013352105 & -0.0308981561 &  0.0 & -0.33871590 & -0.0915822665 &  0.147635999 &  0.006012042 &  0.00000000 & -0.103444736 &  0.0000000000 &  0.5\\\\\n",
       "\t 0 & 1999 &  0.024479115 &  0.0199589931 & -0.5 &  0.34363431 &  0.1051332959 &  0.123904093 & -0.003001503 &  0.18721154 &  0.316258976 &  0.0000000000 &  0.0\\\\\n",
       "\t 0 & 2000 &  0.010736315 &  0.0051233463 &  0.5 &  0.45901574 &  0.0628943640 &  0.090286847 & -0.007038742 &  0.13657554 & -0.310307387 &  0.0195966413 &  0.0\\\\\n",
       "\t 0 & 2001 & -0.012860210 & -0.0130581957 & -0.5 & -0.10950287 & -0.1242445967 &  0.084129531 & -0.007088637 &  0.00000000 & -0.261116345 &  0.0000000000 &  0.0\\\\\n",
       "\t 0 & 2002 &  0.014477534 &  0.0245535667 &  0.5 &  0.03522143 & -0.0377329263 &  0.078820960 & -0.009188426 &  0.06187540 & -0.196926170 &  0.0000000000 &  0.0\\\\\n",
       "\t 1 & 2003 & -0.009538650 &  0.0039634439 &  0.0 &  0.09713002 &  0.0843496712 &  0.075329719 & -0.003081667 &  0.07696104 &  0.221828367 &  0.0170148206 &  0.0\\\\\n",
       "\t 0 & 2004 &  0.013203359 & -0.0004923830 & -0.5 &  0.20097118 &  0.0890274168 &  0.082655722 &  0.000000000 & -0.01869213 &  0.073310952 &  0.0000000000 &  0.0\\\\\n",
       "\t 0 & 2005 & -0.004302008 &  0.0087232322 &  0.5 &  0.31611580 & -0.0094022598 &  0.044905504 & -0.002059733 & -0.12014431 &  0.341250994 &  0.2223047186 & -0.5\\\\\n",
       "\t 0 & 2006 &  0.002756154 & -0.0097746472 &  0.0 &  0.16065287 & -0.0484778223 &  0.002989539 &  0.002059733 & -0.06595797 &  0.063789817 &  0.0000000000 &  0.5\\\\\n",
       "\t 1 & 2007 & -0.017173710 &  0.0245622798 & -0.5 &  0.04150997 & -0.0002505979 &  0.014815086 &  0.000000000 & -0.07061757 & -0.118048778 &  0.0000000000 &  0.0\\\\\n",
       "\t 0 & 2008 & -0.072976198 & -0.0535455057 &  0.5 &  0.23562571 &  0.1064363396 &  0.049723435 &  0.014300550 & -0.05001042 & -0.561164650 &  0.0000000000 & -0.5\\\\\n",
       "\t 0 & 2009 & -0.018964601 & -0.0709879183 &  0.0 & -0.38749772 &  0.0376843968 &  0.118611879 & -0.014300550 &  0.02531781 &  0.188591815 & -0.9112279213 &  0.5\\\\\n",
       "\t 1 & 2010 &  0.042285908 &  0.0455902315 &  0.0 &  0.21075852 &  0.0715071253 &  0.054808236 & -0.007227703 &  0.24294618 & -0.023341674 &  0.0000000000 & -0.5\\\\\n",
       "\t 0 & 2011 & -0.027442840 &  0.0337387012 &  0.0 &  0.17035756 &  0.0914818714 &  0.061593011 & -0.002074690 &  0.00000000 & -0.188344934 &  0.0000000000 &  0.5\\\\\n",
       "\t 0 & 2012 & -0.012998704 &  0.0571105641 &  0.0 &  0.01671820 &  0.0146306523 &  0.049632624 & -0.001038961 & -0.10318424 &  0.207581791 &  0.9044428289 & -0.5\\\\\n",
       "\t 0 & 2013 & -0.007562834 & -0.1167735606 &  0.0 & -0.00462769 & -0.1828863972 &  0.028365790 &  0.004149384 & -0.06744128 &  0.445140246 &  0.0000000000 &  0.0\\\\\n",
       "\t 1 & 2014 & -0.032698672 & -0.0364950713 &  0.0 & -0.06275503 & -0.0603498861 &  0.019588603 &  0.026559273 & -0.07232066 &  0.042192672 &  0.0002122224 &  0.5\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 28 × 13 of type dbl\n",
       "\n",
       "| id | time | Y1 | Y2 | Y3 | Y4 | Y5 | Y6 | Y7 | Y8 | Y9 | Y10 | Y11 |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 0 | 1987 |  0.046607215 | -0.0002078956 |  0.0 |  0.14329221 |  0.1707078336 | -0.011862535 |  0.000000000 |  0.07410797 |  0.142432215 |  0.0000000000 |  0.0 |\n",
       "| 0 | 1988 |  0.054685886 |  0.0659408662 |  0.0 | -0.27232247 |  0.1875246945 | -0.036456042 |  0.006960585 |  0.00000000 |  0.328497539 |  0.0000000000 |  0.0 |\n",
       "| 0 | 1989 |  0.034524371 |  0.0250528266 |  0.0 |  0.19834206 |  0.0005499123 | -0.042990185 |  0.022858138 | -0.11332869 |  0.232060601 |  0.0000000000 |  0.0 |\n",
       "| 0 | 1990 |  0.038998671 |  0.0696834290 |  0.0 |  0.21128783 |  0.0213534086 | -0.050341755 |  0.030052345 | -0.08338161 | -0.519724355 | -0.0870144800 |  0.0 |\n",
       "| 0 | 1991 |  0.014394372 |  0.0108691005 | -0.5 | -0.15698568 |  0.1077704117 | -0.016438726 |  0.033426293 | -0.09097178 | -0.070368088 |  0.0000000000 |  0.0 |\n",
       "| 0 | 1992 |  0.008714600 |  0.0088384495 |  0.5 | -0.03704127 |  0.0555237346 |  0.019152432 |  0.015781495 |  0.00000000 | -0.321780974 | -0.2076436613 | -0.5 |\n",
       "| 0 | 1993 |  0.010772713 |  0.0018245193 |  0.0 | -0.15539469 |  0.1027742980 |  0.078164773 |  0.013478690 |  0.04652002 |  0.015192971 |  0.0000000000 |  0.5 |\n",
       "| 0 | 1994 |  0.034959773 |  0.0388160427 |  0.0 | -0.02806837 |  0.0898896168 |  0.029631798 |  0.006160184 |  0.12783337 |  0.118167799 |  0.0000000000 | -0.5 |\n",
       "| 0 | 1995 |  0.031159245 |  0.0255089921 | -0.5 | -0.01487016 |  0.0913040437 |  0.059049029 | -0.001024066 |  0.14842001 |  0.008353525 |  0.0000000000 |  0.5 |\n",
       "| 0 | 1996 |  0.015857216 |  0.0017373256 |  0.5 |  0.19217123 | -0.1259988250 |  0.060084811 |  0.001024066 |  0.09844007 | -0.026863215 |  0.0868586003 |  0.0 |\n",
       "| 0 | 1997 |  0.007429288 | -0.0068458719 |  0.0 | -0.01512484 | -0.0896433402 |  0.044357853 |  0.018256085 |  0.06062462 | -0.256382432 |  0.0000000000 | -0.5 |\n",
       "| 0 | 1998 | -0.013352105 | -0.0308981561 |  0.0 | -0.33871590 | -0.0915822665 |  0.147635999 |  0.006012042 |  0.00000000 | -0.103444736 |  0.0000000000 |  0.5 |\n",
       "| 0 | 1999 |  0.024479115 |  0.0199589931 | -0.5 |  0.34363431 |  0.1051332959 |  0.123904093 | -0.003001503 |  0.18721154 |  0.316258976 |  0.0000000000 |  0.0 |\n",
       "| 0 | 2000 |  0.010736315 |  0.0051233463 |  0.5 |  0.45901574 |  0.0628943640 |  0.090286847 | -0.007038742 |  0.13657554 | -0.310307387 |  0.0195966413 |  0.0 |\n",
       "| 0 | 2001 | -0.012860210 | -0.0130581957 | -0.5 | -0.10950287 | -0.1242445967 |  0.084129531 | -0.007088637 |  0.00000000 | -0.261116345 |  0.0000000000 |  0.0 |\n",
       "| 0 | 2002 |  0.014477534 |  0.0245535667 |  0.5 |  0.03522143 | -0.0377329263 |  0.078820960 | -0.009188426 |  0.06187540 | -0.196926170 |  0.0000000000 |  0.0 |\n",
       "| 1 | 2003 | -0.009538650 |  0.0039634439 |  0.0 |  0.09713002 |  0.0843496712 |  0.075329719 | -0.003081667 |  0.07696104 |  0.221828367 |  0.0170148206 |  0.0 |\n",
       "| 0 | 2004 |  0.013203359 | -0.0004923830 | -0.5 |  0.20097118 |  0.0890274168 |  0.082655722 |  0.000000000 | -0.01869213 |  0.073310952 |  0.0000000000 |  0.0 |\n",
       "| 0 | 2005 | -0.004302008 |  0.0087232322 |  0.5 |  0.31611580 | -0.0094022598 |  0.044905504 | -0.002059733 | -0.12014431 |  0.341250994 |  0.2223047186 | -0.5 |\n",
       "| 0 | 2006 |  0.002756154 | -0.0097746472 |  0.0 |  0.16065287 | -0.0484778223 |  0.002989539 |  0.002059733 | -0.06595797 |  0.063789817 |  0.0000000000 |  0.5 |\n",
       "| 1 | 2007 | -0.017173710 |  0.0245622798 | -0.5 |  0.04150997 | -0.0002505979 |  0.014815086 |  0.000000000 | -0.07061757 | -0.118048778 |  0.0000000000 |  0.0 |\n",
       "| 0 | 2008 | -0.072976198 | -0.0535455057 |  0.5 |  0.23562571 |  0.1064363396 |  0.049723435 |  0.014300550 | -0.05001042 | -0.561164650 |  0.0000000000 | -0.5 |\n",
       "| 0 | 2009 | -0.018964601 | -0.0709879183 |  0.0 | -0.38749772 |  0.0376843968 |  0.118611879 | -0.014300550 |  0.02531781 |  0.188591815 | -0.9112279213 |  0.5 |\n",
       "| 1 | 2010 |  0.042285908 |  0.0455902315 |  0.0 |  0.21075852 |  0.0715071253 |  0.054808236 | -0.007227703 |  0.24294618 | -0.023341674 |  0.0000000000 | -0.5 |\n",
       "| 0 | 2011 | -0.027442840 |  0.0337387012 |  0.0 |  0.17035756 |  0.0914818714 |  0.061593011 | -0.002074690 |  0.00000000 | -0.188344934 |  0.0000000000 |  0.5 |\n",
       "| 0 | 2012 | -0.012998704 |  0.0571105641 |  0.0 |  0.01671820 |  0.0146306523 |  0.049632624 | -0.001038961 | -0.10318424 |  0.207581791 |  0.9044428289 | -0.5 |\n",
       "| 0 | 2013 | -0.007562834 | -0.1167735606 |  0.0 | -0.00462769 | -0.1828863972 |  0.028365790 |  0.004149384 | -0.06744128 |  0.445140246 |  0.0000000000 |  0.0 |\n",
       "| 1 | 2014 | -0.032698672 | -0.0364950713 |  0.0 | -0.06275503 | -0.0603498861 |  0.019588603 |  0.026559273 | -0.07232066 |  0.042192672 |  0.0002122224 |  0.5 |\n",
       "\n"
      ],
      "text/plain": [
       "      id time Y1           Y2            Y3   Y4          Y5           \n",
       " [1,] 0  1987  0.046607215 -0.0002078956  0.0  0.14329221  0.1707078336\n",
       " [2,] 0  1988  0.054685886  0.0659408662  0.0 -0.27232247  0.1875246945\n",
       " [3,] 0  1989  0.034524371  0.0250528266  0.0  0.19834206  0.0005499123\n",
       " [4,] 0  1990  0.038998671  0.0696834290  0.0  0.21128783  0.0213534086\n",
       " [5,] 0  1991  0.014394372  0.0108691005 -0.5 -0.15698568  0.1077704117\n",
       " [6,] 0  1992  0.008714600  0.0088384495  0.5 -0.03704127  0.0555237346\n",
       " [7,] 0  1993  0.010772713  0.0018245193  0.0 -0.15539469  0.1027742980\n",
       " [8,] 0  1994  0.034959773  0.0388160427  0.0 -0.02806837  0.0898896168\n",
       " [9,] 0  1995  0.031159245  0.0255089921 -0.5 -0.01487016  0.0913040437\n",
       "[10,] 0  1996  0.015857216  0.0017373256  0.5  0.19217123 -0.1259988250\n",
       "[11,] 0  1997  0.007429288 -0.0068458719  0.0 -0.01512484 -0.0896433402\n",
       "[12,] 0  1998 -0.013352105 -0.0308981561  0.0 -0.33871590 -0.0915822665\n",
       "[13,] 0  1999  0.024479115  0.0199589931 -0.5  0.34363431  0.1051332959\n",
       "[14,] 0  2000  0.010736315  0.0051233463  0.5  0.45901574  0.0628943640\n",
       "[15,] 0  2001 -0.012860210 -0.0130581957 -0.5 -0.10950287 -0.1242445967\n",
       "[16,] 0  2002  0.014477534  0.0245535667  0.5  0.03522143 -0.0377329263\n",
       "[17,] 1  2003 -0.009538650  0.0039634439  0.0  0.09713002  0.0843496712\n",
       "[18,] 0  2004  0.013203359 -0.0004923830 -0.5  0.20097118  0.0890274168\n",
       "[19,] 0  2005 -0.004302008  0.0087232322  0.5  0.31611580 -0.0094022598\n",
       "[20,] 0  2006  0.002756154 -0.0097746472  0.0  0.16065287 -0.0484778223\n",
       "[21,] 1  2007 -0.017173710  0.0245622798 -0.5  0.04150997 -0.0002505979\n",
       "[22,] 0  2008 -0.072976198 -0.0535455057  0.5  0.23562571  0.1064363396\n",
       "[23,] 0  2009 -0.018964601 -0.0709879183  0.0 -0.38749772  0.0376843968\n",
       "[24,] 1  2010  0.042285908  0.0455902315  0.0  0.21075852  0.0715071253\n",
       "[25,] 0  2011 -0.027442840  0.0337387012  0.0  0.17035756  0.0914818714\n",
       "[26,] 0  2012 -0.012998704  0.0571105641  0.0  0.01671820  0.0146306523\n",
       "[27,] 0  2013 -0.007562834 -0.1167735606  0.0 -0.00462769 -0.1828863972\n",
       "[28,] 1  2014 -0.032698672 -0.0364950713  0.0 -0.06275503 -0.0603498861\n",
       "      Y6           Y7           Y8          Y9           Y10           Y11 \n",
       " [1,] -0.011862535  0.000000000  0.07410797  0.142432215  0.0000000000  0.0\n",
       " [2,] -0.036456042  0.006960585  0.00000000  0.328497539  0.0000000000  0.0\n",
       " [3,] -0.042990185  0.022858138 -0.11332869  0.232060601  0.0000000000  0.0\n",
       " [4,] -0.050341755  0.030052345 -0.08338161 -0.519724355 -0.0870144800  0.0\n",
       " [5,] -0.016438726  0.033426293 -0.09097178 -0.070368088  0.0000000000  0.0\n",
       " [6,]  0.019152432  0.015781495  0.00000000 -0.321780974 -0.2076436613 -0.5\n",
       " [7,]  0.078164773  0.013478690  0.04652002  0.015192971  0.0000000000  0.5\n",
       " [8,]  0.029631798  0.006160184  0.12783337  0.118167799  0.0000000000 -0.5\n",
       " [9,]  0.059049029 -0.001024066  0.14842001  0.008353525  0.0000000000  0.5\n",
       "[10,]  0.060084811  0.001024066  0.09844007 -0.026863215  0.0868586003  0.0\n",
       "[11,]  0.044357853  0.018256085  0.06062462 -0.256382432  0.0000000000 -0.5\n",
       "[12,]  0.147635999  0.006012042  0.00000000 -0.103444736  0.0000000000  0.5\n",
       "[13,]  0.123904093 -0.003001503  0.18721154  0.316258976  0.0000000000  0.0\n",
       "[14,]  0.090286847 -0.007038742  0.13657554 -0.310307387  0.0195966413  0.0\n",
       "[15,]  0.084129531 -0.007088637  0.00000000 -0.261116345  0.0000000000  0.0\n",
       "[16,]  0.078820960 -0.009188426  0.06187540 -0.196926170  0.0000000000  0.0\n",
       "[17,]  0.075329719 -0.003081667  0.07696104  0.221828367  0.0170148206  0.0\n",
       "[18,]  0.082655722  0.000000000 -0.01869213  0.073310952  0.0000000000  0.0\n",
       "[19,]  0.044905504 -0.002059733 -0.12014431  0.341250994  0.2223047186 -0.5\n",
       "[20,]  0.002989539  0.002059733 -0.06595797  0.063789817  0.0000000000  0.5\n",
       "[21,]  0.014815086  0.000000000 -0.07061757 -0.118048778  0.0000000000  0.0\n",
       "[22,]  0.049723435  0.014300550 -0.05001042 -0.561164650  0.0000000000 -0.5\n",
       "[23,]  0.118611879 -0.014300550  0.02531781  0.188591815 -0.9112279213  0.5\n",
       "[24,]  0.054808236 -0.007227703  0.24294618 -0.023341674  0.0000000000 -0.5\n",
       "[25,]  0.061593011 -0.002074690  0.00000000 -0.188344934  0.0000000000  0.5\n",
       "[26,]  0.049632624 -0.001038961 -0.10318424  0.207581791  0.9044428289 -0.5\n",
       "[27,]  0.028365790  0.004149384 -0.06744128  0.445140246  0.0000000000  0.0\n",
       "[28,]  0.019588603  0.026559273 -0.07232066  0.042192672  0.0002122224  0.5"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "adjusted <- bind_cols(\n",
    "    raw_data$id[-1]\n",
    "    , time = raw_data$time[-1]\n",
    "    , Y1   = diff.log(raw_data$Y1)\n",
    "    , Y2   = diff.log(raw_data$Y2)\n",
    "    , Y3   = probit(raw_data$Y3) %>% diff.log\n",
    "    , Y4   = diff.log(raw_data$Y4)\n",
    "    , Y5   = diff.log(raw_data$Y5)\n",
    "    , Y6   = diff.log(raw_data$Y6)\n",
    "    , Y7   = diff.log(raw_data$Y7)\n",
    "    , Y8   = diff.log(raw_data$Y8)\n",
    "    , Y9   = diff.log(raw_data$Y9)\n",
    "    , Y10  = diff.log(raw_data$Y10)\n",
    "    , Y11  = probit(raw_data$Y11) %>% diff.log\n",
    ") %>%\n",
    "  as.data.frame %>%\n",
    "  apply(2, as.numeric)\n",
    "#列名を戻す。\n",
    "colnames(adjusted) <- colnames(raw_data)\n",
    "#目視確認\n",
    "adjusted"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 多重共線性の実証分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 28 × 11</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Y1</th><th scope=col>Y2</th><th scope=col>Y3</th><th scope=col>Y4</th><th scope=col>Y5</th><th scope=col>Y6</th><th scope=col>Y7</th><th scope=col>Y8</th><th scope=col>Y9</th><th scope=col>Y10</th><th scope=col>Y11</th></tr>\n",
       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td> 0.046607215</td><td>-0.0002078956</td><td> 0.0</td><td> 0.14329221</td><td> 0.1707078336</td><td>-0.011862535</td><td> 0.000000000</td><td> 0.07410797</td><td> 0.142432215</td><td> 0.0000000000</td><td> 0.0</td></tr>\n",
       "\t<tr><td> 0.054685886</td><td> 0.0659408662</td><td> 0.0</td><td>-0.27232247</td><td> 0.1875246945</td><td>-0.036456042</td><td> 0.006960585</td><td> 0.00000000</td><td> 0.328497539</td><td> 0.0000000000</td><td> 0.0</td></tr>\n",
       "\t<tr><td> 0.034524371</td><td> 0.0250528266</td><td> 0.0</td><td> 0.19834206</td><td> 0.0005499123</td><td>-0.042990185</td><td> 0.022858138</td><td>-0.11332869</td><td> 0.232060601</td><td> 0.0000000000</td><td> 0.0</td></tr>\n",
       "\t<tr><td> 0.038998671</td><td> 0.0696834290</td><td> 0.0</td><td> 0.21128783</td><td> 0.0213534086</td><td>-0.050341755</td><td> 0.030052345</td><td>-0.08338161</td><td>-0.519724355</td><td>-0.0870144800</td><td> 0.0</td></tr>\n",
       "\t<tr><td> 0.014394372</td><td> 0.0108691005</td><td>-0.5</td><td>-0.15698568</td><td> 0.1077704117</td><td>-0.016438726</td><td> 0.033426293</td><td>-0.09097178</td><td>-0.070368088</td><td> 0.0000000000</td><td> 0.0</td></tr>\n",
       "\t<tr><td> 0.008714600</td><td> 0.0088384495</td><td> 0.5</td><td>-0.03704127</td><td> 0.0555237346</td><td> 0.019152432</td><td> 0.015781495</td><td> 0.00000000</td><td>-0.321780974</td><td>-0.2076436613</td><td>-0.5</td></tr>\n",
       "\t<tr><td> 0.010772713</td><td> 0.0018245193</td><td> 0.0</td><td>-0.15539469</td><td> 0.1027742980</td><td> 0.078164773</td><td> 0.013478690</td><td> 0.04652002</td><td> 0.015192971</td><td> 0.0000000000</td><td> 0.5</td></tr>\n",
       "\t<tr><td> 0.034959773</td><td> 0.0388160427</td><td> 0.0</td><td>-0.02806837</td><td> 0.0898896168</td><td> 0.029631798</td><td> 0.006160184</td><td> 0.12783337</td><td> 0.118167799</td><td> 0.0000000000</td><td>-0.5</td></tr>\n",
       "\t<tr><td> 0.031159245</td><td> 0.0255089921</td><td>-0.5</td><td>-0.01487016</td><td> 0.0913040437</td><td> 0.059049029</td><td>-0.001024066</td><td> 0.14842001</td><td> 0.008353525</td><td> 0.0000000000</td><td> 0.5</td></tr>\n",
       "\t<tr><td> 0.015857216</td><td> 0.0017373256</td><td> 0.5</td><td> 0.19217123</td><td>-0.1259988250</td><td> 0.060084811</td><td> 0.001024066</td><td> 0.09844007</td><td>-0.026863215</td><td> 0.0868586003</td><td> 0.0</td></tr>\n",
       "\t<tr><td> 0.007429288</td><td>-0.0068458719</td><td> 0.0</td><td>-0.01512484</td><td>-0.0896433402</td><td> 0.044357853</td><td> 0.018256085</td><td> 0.06062462</td><td>-0.256382432</td><td> 0.0000000000</td><td>-0.5</td></tr>\n",
       "\t<tr><td>-0.013352105</td><td>-0.0308981561</td><td> 0.0</td><td>-0.33871590</td><td>-0.0915822665</td><td> 0.147635999</td><td> 0.006012042</td><td> 0.00000000</td><td>-0.103444736</td><td> 0.0000000000</td><td> 0.5</td></tr>\n",
       "\t<tr><td> 0.024479115</td><td> 0.0199589931</td><td>-0.5</td><td> 0.34363431</td><td> 0.1051332959</td><td> 0.123904093</td><td>-0.003001503</td><td> 0.18721154</td><td> 0.316258976</td><td> 0.0000000000</td><td> 0.0</td></tr>\n",
       "\t<tr><td> 0.010736315</td><td> 0.0051233463</td><td> 0.5</td><td> 0.45901574</td><td> 0.0628943640</td><td> 0.090286847</td><td>-0.007038742</td><td> 0.13657554</td><td>-0.310307387</td><td> 0.0195966413</td><td> 0.0</td></tr>\n",
       "\t<tr><td>-0.012860210</td><td>-0.0130581957</td><td>-0.5</td><td>-0.10950287</td><td>-0.1242445967</td><td> 0.084129531</td><td>-0.007088637</td><td> 0.00000000</td><td>-0.261116345</td><td> 0.0000000000</td><td> 0.0</td></tr>\n",
       "\t<tr><td> 0.014477534</td><td> 0.0245535667</td><td> 0.5</td><td> 0.03522143</td><td>-0.0377329263</td><td> 0.078820960</td><td>-0.009188426</td><td> 0.06187540</td><td>-0.196926170</td><td> 0.0000000000</td><td> 0.0</td></tr>\n",
       "\t<tr><td>-0.009538650</td><td> 0.0039634439</td><td> 0.0</td><td> 0.09713002</td><td> 0.0843496712</td><td> 0.075329719</td><td>-0.003081667</td><td> 0.07696104</td><td> 0.221828367</td><td> 0.0170148206</td><td> 0.0</td></tr>\n",
       "\t<tr><td> 0.013203359</td><td>-0.0004923830</td><td>-0.5</td><td> 0.20097118</td><td> 0.0890274168</td><td> 0.082655722</td><td> 0.000000000</td><td>-0.01869213</td><td> 0.073310952</td><td> 0.0000000000</td><td> 0.0</td></tr>\n",
       "\t<tr><td>-0.004302008</td><td> 0.0087232322</td><td> 0.5</td><td> 0.31611580</td><td>-0.0094022598</td><td> 0.044905504</td><td>-0.002059733</td><td>-0.12014431</td><td> 0.341250994</td><td> 0.2223047186</td><td>-0.5</td></tr>\n",
       "\t<tr><td> 0.002756154</td><td>-0.0097746472</td><td> 0.0</td><td> 0.16065287</td><td>-0.0484778223</td><td> 0.002989539</td><td> 0.002059733</td><td>-0.06595797</td><td> 0.063789817</td><td> 0.0000000000</td><td> 0.5</td></tr>\n",
       "\t<tr><td>-0.017173710</td><td> 0.0245622798</td><td>-0.5</td><td> 0.04150997</td><td>-0.0002505979</td><td> 0.014815086</td><td> 0.000000000</td><td>-0.07061757</td><td>-0.118048778</td><td> 0.0000000000</td><td> 0.0</td></tr>\n",
       "\t<tr><td>-0.072976198</td><td>-0.0535455057</td><td> 0.5</td><td> 0.23562571</td><td> 0.1064363396</td><td> 0.049723435</td><td> 0.014300550</td><td>-0.05001042</td><td>-0.561164650</td><td> 0.0000000000</td><td>-0.5</td></tr>\n",
       "\t<tr><td>-0.018964601</td><td>-0.0709879183</td><td> 0.0</td><td>-0.38749772</td><td> 0.0376843968</td><td> 0.118611879</td><td>-0.014300550</td><td> 0.02531781</td><td> 0.188591815</td><td>-0.9112279213</td><td> 0.5</td></tr>\n",
       "\t<tr><td> 0.042285908</td><td> 0.0455902315</td><td> 0.0</td><td> 0.21075852</td><td> 0.0715071253</td><td> 0.054808236</td><td>-0.007227703</td><td> 0.24294618</td><td>-0.023341674</td><td> 0.0000000000</td><td>-0.5</td></tr>\n",
       "\t<tr><td>-0.027442840</td><td> 0.0337387012</td><td> 0.0</td><td> 0.17035756</td><td> 0.0914818714</td><td> 0.061593011</td><td>-0.002074690</td><td> 0.00000000</td><td>-0.188344934</td><td> 0.0000000000</td><td> 0.5</td></tr>\n",
       "\t<tr><td>-0.012998704</td><td> 0.0571105641</td><td> 0.0</td><td> 0.01671820</td><td> 0.0146306523</td><td> 0.049632624</td><td>-0.001038961</td><td>-0.10318424</td><td> 0.207581791</td><td> 0.9044428289</td><td>-0.5</td></tr>\n",
       "\t<tr><td>-0.007562834</td><td>-0.1167735606</td><td> 0.0</td><td>-0.00462769</td><td>-0.1828863972</td><td> 0.028365790</td><td> 0.004149384</td><td>-0.06744128</td><td> 0.445140246</td><td> 0.0000000000</td><td> 0.0</td></tr>\n",
       "\t<tr><td>-0.032698672</td><td>-0.0364950713</td><td> 0.0</td><td>-0.06275503</td><td>-0.0603498861</td><td> 0.019588603</td><td> 0.026559273</td><td>-0.07232066</td><td> 0.042192672</td><td> 0.0002122224</td><td> 0.5</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 28 × 11\n",
       "\\begin{tabular}{lllllllllll}\n",
       " Y1 & Y2 & Y3 & Y4 & Y5 & Y6 & Y7 & Y8 & Y9 & Y10 & Y11\\\\\n",
       " <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t  0.046607215 & -0.0002078956 &  0.0 &  0.14329221 &  0.1707078336 & -0.011862535 &  0.000000000 &  0.07410797 &  0.142432215 &  0.0000000000 &  0.0\\\\\n",
       "\t  0.054685886 &  0.0659408662 &  0.0 & -0.27232247 &  0.1875246945 & -0.036456042 &  0.006960585 &  0.00000000 &  0.328497539 &  0.0000000000 &  0.0\\\\\n",
       "\t  0.034524371 &  0.0250528266 &  0.0 &  0.19834206 &  0.0005499123 & -0.042990185 &  0.022858138 & -0.11332869 &  0.232060601 &  0.0000000000 &  0.0\\\\\n",
       "\t  0.038998671 &  0.0696834290 &  0.0 &  0.21128783 &  0.0213534086 & -0.050341755 &  0.030052345 & -0.08338161 & -0.519724355 & -0.0870144800 &  0.0\\\\\n",
       "\t  0.014394372 &  0.0108691005 & -0.5 & -0.15698568 &  0.1077704117 & -0.016438726 &  0.033426293 & -0.09097178 & -0.070368088 &  0.0000000000 &  0.0\\\\\n",
       "\t  0.008714600 &  0.0088384495 &  0.5 & -0.03704127 &  0.0555237346 &  0.019152432 &  0.015781495 &  0.00000000 & -0.321780974 & -0.2076436613 & -0.5\\\\\n",
       "\t  0.010772713 &  0.0018245193 &  0.0 & -0.15539469 &  0.1027742980 &  0.078164773 &  0.013478690 &  0.04652002 &  0.015192971 &  0.0000000000 &  0.5\\\\\n",
       "\t  0.034959773 &  0.0388160427 &  0.0 & -0.02806837 &  0.0898896168 &  0.029631798 &  0.006160184 &  0.12783337 &  0.118167799 &  0.0000000000 & -0.5\\\\\n",
       "\t  0.031159245 &  0.0255089921 & -0.5 & -0.01487016 &  0.0913040437 &  0.059049029 & -0.001024066 &  0.14842001 &  0.008353525 &  0.0000000000 &  0.5\\\\\n",
       "\t  0.015857216 &  0.0017373256 &  0.5 &  0.19217123 & -0.1259988250 &  0.060084811 &  0.001024066 &  0.09844007 & -0.026863215 &  0.0868586003 &  0.0\\\\\n",
       "\t  0.007429288 & -0.0068458719 &  0.0 & -0.01512484 & -0.0896433402 &  0.044357853 &  0.018256085 &  0.06062462 & -0.256382432 &  0.0000000000 & -0.5\\\\\n",
       "\t -0.013352105 & -0.0308981561 &  0.0 & -0.33871590 & -0.0915822665 &  0.147635999 &  0.006012042 &  0.00000000 & -0.103444736 &  0.0000000000 &  0.5\\\\\n",
       "\t  0.024479115 &  0.0199589931 & -0.5 &  0.34363431 &  0.1051332959 &  0.123904093 & -0.003001503 &  0.18721154 &  0.316258976 &  0.0000000000 &  0.0\\\\\n",
       "\t  0.010736315 &  0.0051233463 &  0.5 &  0.45901574 &  0.0628943640 &  0.090286847 & -0.007038742 &  0.13657554 & -0.310307387 &  0.0195966413 &  0.0\\\\\n",
       "\t -0.012860210 & -0.0130581957 & -0.5 & -0.10950287 & -0.1242445967 &  0.084129531 & -0.007088637 &  0.00000000 & -0.261116345 &  0.0000000000 &  0.0\\\\\n",
       "\t  0.014477534 &  0.0245535667 &  0.5 &  0.03522143 & -0.0377329263 &  0.078820960 & -0.009188426 &  0.06187540 & -0.196926170 &  0.0000000000 &  0.0\\\\\n",
       "\t -0.009538650 &  0.0039634439 &  0.0 &  0.09713002 &  0.0843496712 &  0.075329719 & -0.003081667 &  0.07696104 &  0.221828367 &  0.0170148206 &  0.0\\\\\n",
       "\t  0.013203359 & -0.0004923830 & -0.5 &  0.20097118 &  0.0890274168 &  0.082655722 &  0.000000000 & -0.01869213 &  0.073310952 &  0.0000000000 &  0.0\\\\\n",
       "\t -0.004302008 &  0.0087232322 &  0.5 &  0.31611580 & -0.0094022598 &  0.044905504 & -0.002059733 & -0.12014431 &  0.341250994 &  0.2223047186 & -0.5\\\\\n",
       "\t  0.002756154 & -0.0097746472 &  0.0 &  0.16065287 & -0.0484778223 &  0.002989539 &  0.002059733 & -0.06595797 &  0.063789817 &  0.0000000000 &  0.5\\\\\n",
       "\t -0.017173710 &  0.0245622798 & -0.5 &  0.04150997 & -0.0002505979 &  0.014815086 &  0.000000000 & -0.07061757 & -0.118048778 &  0.0000000000 &  0.0\\\\\n",
       "\t -0.072976198 & -0.0535455057 &  0.5 &  0.23562571 &  0.1064363396 &  0.049723435 &  0.014300550 & -0.05001042 & -0.561164650 &  0.0000000000 & -0.5\\\\\n",
       "\t -0.018964601 & -0.0709879183 &  0.0 & -0.38749772 &  0.0376843968 &  0.118611879 & -0.014300550 &  0.02531781 &  0.188591815 & -0.9112279213 &  0.5\\\\\n",
       "\t  0.042285908 &  0.0455902315 &  0.0 &  0.21075852 &  0.0715071253 &  0.054808236 & -0.007227703 &  0.24294618 & -0.023341674 &  0.0000000000 & -0.5\\\\\n",
       "\t -0.027442840 &  0.0337387012 &  0.0 &  0.17035756 &  0.0914818714 &  0.061593011 & -0.002074690 &  0.00000000 & -0.188344934 &  0.0000000000 &  0.5\\\\\n",
       "\t -0.012998704 &  0.0571105641 &  0.0 &  0.01671820 &  0.0146306523 &  0.049632624 & -0.001038961 & -0.10318424 &  0.207581791 &  0.9044428289 & -0.5\\\\\n",
       "\t -0.007562834 & -0.1167735606 &  0.0 & -0.00462769 & -0.1828863972 &  0.028365790 &  0.004149384 & -0.06744128 &  0.445140246 &  0.0000000000 &  0.0\\\\\n",
       "\t -0.032698672 & -0.0364950713 &  0.0 & -0.06275503 & -0.0603498861 &  0.019588603 &  0.026559273 & -0.07232066 &  0.042192672 &  0.0002122224 &  0.5\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 28 × 11\n",
       "\n",
       "| Y1 &lt;dbl&gt; | Y2 &lt;dbl&gt; | Y3 &lt;dbl&gt; | Y4 &lt;dbl&gt; | Y5 &lt;dbl&gt; | Y6 &lt;dbl&gt; | Y7 &lt;dbl&gt; | Y8 &lt;dbl&gt; | Y9 &lt;dbl&gt; | Y10 &lt;dbl&gt; | Y11 &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|\n",
       "|  0.046607215 | -0.0002078956 |  0.0 |  0.14329221 |  0.1707078336 | -0.011862535 |  0.000000000 |  0.07410797 |  0.142432215 |  0.0000000000 |  0.0 |\n",
       "|  0.054685886 |  0.0659408662 |  0.0 | -0.27232247 |  0.1875246945 | -0.036456042 |  0.006960585 |  0.00000000 |  0.328497539 |  0.0000000000 |  0.0 |\n",
       "|  0.034524371 |  0.0250528266 |  0.0 |  0.19834206 |  0.0005499123 | -0.042990185 |  0.022858138 | -0.11332869 |  0.232060601 |  0.0000000000 |  0.0 |\n",
       "|  0.038998671 |  0.0696834290 |  0.0 |  0.21128783 |  0.0213534086 | -0.050341755 |  0.030052345 | -0.08338161 | -0.519724355 | -0.0870144800 |  0.0 |\n",
       "|  0.014394372 |  0.0108691005 | -0.5 | -0.15698568 |  0.1077704117 | -0.016438726 |  0.033426293 | -0.09097178 | -0.070368088 |  0.0000000000 |  0.0 |\n",
       "|  0.008714600 |  0.0088384495 |  0.5 | -0.03704127 |  0.0555237346 |  0.019152432 |  0.015781495 |  0.00000000 | -0.321780974 | -0.2076436613 | -0.5 |\n",
       "|  0.010772713 |  0.0018245193 |  0.0 | -0.15539469 |  0.1027742980 |  0.078164773 |  0.013478690 |  0.04652002 |  0.015192971 |  0.0000000000 |  0.5 |\n",
       "|  0.034959773 |  0.0388160427 |  0.0 | -0.02806837 |  0.0898896168 |  0.029631798 |  0.006160184 |  0.12783337 |  0.118167799 |  0.0000000000 | -0.5 |\n",
       "|  0.031159245 |  0.0255089921 | -0.5 | -0.01487016 |  0.0913040437 |  0.059049029 | -0.001024066 |  0.14842001 |  0.008353525 |  0.0000000000 |  0.5 |\n",
       "|  0.015857216 |  0.0017373256 |  0.5 |  0.19217123 | -0.1259988250 |  0.060084811 |  0.001024066 |  0.09844007 | -0.026863215 |  0.0868586003 |  0.0 |\n",
       "|  0.007429288 | -0.0068458719 |  0.0 | -0.01512484 | -0.0896433402 |  0.044357853 |  0.018256085 |  0.06062462 | -0.256382432 |  0.0000000000 | -0.5 |\n",
       "| -0.013352105 | -0.0308981561 |  0.0 | -0.33871590 | -0.0915822665 |  0.147635999 |  0.006012042 |  0.00000000 | -0.103444736 |  0.0000000000 |  0.5 |\n",
       "|  0.024479115 |  0.0199589931 | -0.5 |  0.34363431 |  0.1051332959 |  0.123904093 | -0.003001503 |  0.18721154 |  0.316258976 |  0.0000000000 |  0.0 |\n",
       "|  0.010736315 |  0.0051233463 |  0.5 |  0.45901574 |  0.0628943640 |  0.090286847 | -0.007038742 |  0.13657554 | -0.310307387 |  0.0195966413 |  0.0 |\n",
       "| -0.012860210 | -0.0130581957 | -0.5 | -0.10950287 | -0.1242445967 |  0.084129531 | -0.007088637 |  0.00000000 | -0.261116345 |  0.0000000000 |  0.0 |\n",
       "|  0.014477534 |  0.0245535667 |  0.5 |  0.03522143 | -0.0377329263 |  0.078820960 | -0.009188426 |  0.06187540 | -0.196926170 |  0.0000000000 |  0.0 |\n",
       "| -0.009538650 |  0.0039634439 |  0.0 |  0.09713002 |  0.0843496712 |  0.075329719 | -0.003081667 |  0.07696104 |  0.221828367 |  0.0170148206 |  0.0 |\n",
       "|  0.013203359 | -0.0004923830 | -0.5 |  0.20097118 |  0.0890274168 |  0.082655722 |  0.000000000 | -0.01869213 |  0.073310952 |  0.0000000000 |  0.0 |\n",
       "| -0.004302008 |  0.0087232322 |  0.5 |  0.31611580 | -0.0094022598 |  0.044905504 | -0.002059733 | -0.12014431 |  0.341250994 |  0.2223047186 | -0.5 |\n",
       "|  0.002756154 | -0.0097746472 |  0.0 |  0.16065287 | -0.0484778223 |  0.002989539 |  0.002059733 | -0.06595797 |  0.063789817 |  0.0000000000 |  0.5 |\n",
       "| -0.017173710 |  0.0245622798 | -0.5 |  0.04150997 | -0.0002505979 |  0.014815086 |  0.000000000 | -0.07061757 | -0.118048778 |  0.0000000000 |  0.0 |\n",
       "| -0.072976198 | -0.0535455057 |  0.5 |  0.23562571 |  0.1064363396 |  0.049723435 |  0.014300550 | -0.05001042 | -0.561164650 |  0.0000000000 | -0.5 |\n",
       "| -0.018964601 | -0.0709879183 |  0.0 | -0.38749772 |  0.0376843968 |  0.118611879 | -0.014300550 |  0.02531781 |  0.188591815 | -0.9112279213 |  0.5 |\n",
       "|  0.042285908 |  0.0455902315 |  0.0 |  0.21075852 |  0.0715071253 |  0.054808236 | -0.007227703 |  0.24294618 | -0.023341674 |  0.0000000000 | -0.5 |\n",
       "| -0.027442840 |  0.0337387012 |  0.0 |  0.17035756 |  0.0914818714 |  0.061593011 | -0.002074690 |  0.00000000 | -0.188344934 |  0.0000000000 |  0.5 |\n",
       "| -0.012998704 |  0.0571105641 |  0.0 |  0.01671820 |  0.0146306523 |  0.049632624 | -0.001038961 | -0.10318424 |  0.207581791 |  0.9044428289 | -0.5 |\n",
       "| -0.007562834 | -0.1167735606 |  0.0 | -0.00462769 | -0.1828863972 |  0.028365790 |  0.004149384 | -0.06744128 |  0.445140246 |  0.0000000000 |  0.0 |\n",
       "| -0.032698672 | -0.0364950713 |  0.0 | -0.06275503 | -0.0603498861 |  0.019588603 |  0.026559273 | -0.07232066 |  0.042192672 |  0.0002122224 |  0.5 |\n",
       "\n"
      ],
      "text/plain": [
       "   Y1           Y2            Y3   Y4          Y5            Y6          \n",
       "1   0.046607215 -0.0002078956  0.0  0.14329221  0.1707078336 -0.011862535\n",
       "2   0.054685886  0.0659408662  0.0 -0.27232247  0.1875246945 -0.036456042\n",
       "3   0.034524371  0.0250528266  0.0  0.19834206  0.0005499123 -0.042990185\n",
       "4   0.038998671  0.0696834290  0.0  0.21128783  0.0213534086 -0.050341755\n",
       "5   0.014394372  0.0108691005 -0.5 -0.15698568  0.1077704117 -0.016438726\n",
       "6   0.008714600  0.0088384495  0.5 -0.03704127  0.0555237346  0.019152432\n",
       "7   0.010772713  0.0018245193  0.0 -0.15539469  0.1027742980  0.078164773\n",
       "8   0.034959773  0.0388160427  0.0 -0.02806837  0.0898896168  0.029631798\n",
       "9   0.031159245  0.0255089921 -0.5 -0.01487016  0.0913040437  0.059049029\n",
       "10  0.015857216  0.0017373256  0.5  0.19217123 -0.1259988250  0.060084811\n",
       "11  0.007429288 -0.0068458719  0.0 -0.01512484 -0.0896433402  0.044357853\n",
       "12 -0.013352105 -0.0308981561  0.0 -0.33871590 -0.0915822665  0.147635999\n",
       "13  0.024479115  0.0199589931 -0.5  0.34363431  0.1051332959  0.123904093\n",
       "14  0.010736315  0.0051233463  0.5  0.45901574  0.0628943640  0.090286847\n",
       "15 -0.012860210 -0.0130581957 -0.5 -0.10950287 -0.1242445967  0.084129531\n",
       "16  0.014477534  0.0245535667  0.5  0.03522143 -0.0377329263  0.078820960\n",
       "17 -0.009538650  0.0039634439  0.0  0.09713002  0.0843496712  0.075329719\n",
       "18  0.013203359 -0.0004923830 -0.5  0.20097118  0.0890274168  0.082655722\n",
       "19 -0.004302008  0.0087232322  0.5  0.31611580 -0.0094022598  0.044905504\n",
       "20  0.002756154 -0.0097746472  0.0  0.16065287 -0.0484778223  0.002989539\n",
       "21 -0.017173710  0.0245622798 -0.5  0.04150997 -0.0002505979  0.014815086\n",
       "22 -0.072976198 -0.0535455057  0.5  0.23562571  0.1064363396  0.049723435\n",
       "23 -0.018964601 -0.0709879183  0.0 -0.38749772  0.0376843968  0.118611879\n",
       "24  0.042285908  0.0455902315  0.0  0.21075852  0.0715071253  0.054808236\n",
       "25 -0.027442840  0.0337387012  0.0  0.17035756  0.0914818714  0.061593011\n",
       "26 -0.012998704  0.0571105641  0.0  0.01671820  0.0146306523  0.049632624\n",
       "27 -0.007562834 -0.1167735606  0.0 -0.00462769 -0.1828863972  0.028365790\n",
       "28 -0.032698672 -0.0364950713  0.0 -0.06275503 -0.0603498861  0.019588603\n",
       "   Y7           Y8          Y9           Y10           Y11 \n",
       "1   0.000000000  0.07410797  0.142432215  0.0000000000  0.0\n",
       "2   0.006960585  0.00000000  0.328497539  0.0000000000  0.0\n",
       "3   0.022858138 -0.11332869  0.232060601  0.0000000000  0.0\n",
       "4   0.030052345 -0.08338161 -0.519724355 -0.0870144800  0.0\n",
       "5   0.033426293 -0.09097178 -0.070368088  0.0000000000  0.0\n",
       "6   0.015781495  0.00000000 -0.321780974 -0.2076436613 -0.5\n",
       "7   0.013478690  0.04652002  0.015192971  0.0000000000  0.5\n",
       "8   0.006160184  0.12783337  0.118167799  0.0000000000 -0.5\n",
       "9  -0.001024066  0.14842001  0.008353525  0.0000000000  0.5\n",
       "10  0.001024066  0.09844007 -0.026863215  0.0868586003  0.0\n",
       "11  0.018256085  0.06062462 -0.256382432  0.0000000000 -0.5\n",
       "12  0.006012042  0.00000000 -0.103444736  0.0000000000  0.5\n",
       "13 -0.003001503  0.18721154  0.316258976  0.0000000000  0.0\n",
       "14 -0.007038742  0.13657554 -0.310307387  0.0195966413  0.0\n",
       "15 -0.007088637  0.00000000 -0.261116345  0.0000000000  0.0\n",
       "16 -0.009188426  0.06187540 -0.196926170  0.0000000000  0.0\n",
       "17 -0.003081667  0.07696104  0.221828367  0.0170148206  0.0\n",
       "18  0.000000000 -0.01869213  0.073310952  0.0000000000  0.0\n",
       "19 -0.002059733 -0.12014431  0.341250994  0.2223047186 -0.5\n",
       "20  0.002059733 -0.06595797  0.063789817  0.0000000000  0.5\n",
       "21  0.000000000 -0.07061757 -0.118048778  0.0000000000  0.0\n",
       "22  0.014300550 -0.05001042 -0.561164650  0.0000000000 -0.5\n",
       "23 -0.014300550  0.02531781  0.188591815 -0.9112279213  0.5\n",
       "24 -0.007227703  0.24294618 -0.023341674  0.0000000000 -0.5\n",
       "25 -0.002074690  0.00000000 -0.188344934  0.0000000000  0.5\n",
       "26 -0.001038961 -0.10318424  0.207581791  0.9044428289 -0.5\n",
       "27  0.004149384 -0.06744128  0.445140246  0.0000000000  0.0\n",
       "28  0.026559273 -0.07232066  0.042192672  0.0002122224  0.5"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#確率変数のみのデータフレーム\n",
    "relation <- adjusted[, !(colnames(adjusted) %in% c(\"id\", \"time\"))] %>%\n",
    "  apply(2, as.numeric) %>%\n",
    "  as.data.frame\n",
    "#目視確認\n",
    "relation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 無相関検定\n",
    "- 相関係数に統計的有意性を検出すれば、多重共線性がある。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "\tPearson's product-moment correlation\n",
       "\n",
       "data:  relation$Y1 and relation$Y2\n",
       "t = 3.5192, df = 26, p-value = 0.001615\n",
       "alternative hypothesis: true correlation is not equal to 0\n",
       "95 percent confidence interval:\n",
       " 0.2473684 0.7765392\n",
       "sample estimates:\n",
       "      cor \n",
       "0.5680239 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tPearson's product-moment correlation\n",
       "\n",
       "data:  relation$Y1 and relation$Y3\n",
       "t = -0.82961, df = 26, p-value = 0.4143\n",
       "alternative hypothesis: true correlation is not equal to 0\n",
       "95 percent confidence interval:\n",
       " -0.5034989  0.2260314\n",
       "sample estimates:\n",
       "       cor \n",
       "-0.1605874 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tPearson's product-moment correlation\n",
       "\n",
       "data:  relation$Y1 and relation$Y4\n",
       "t = 0.37222, df = 26, p-value = 0.7127\n",
       "alternative hypothesis: true correlation is not equal to 0\n",
       "95 percent confidence interval:\n",
       " -0.3086556  0.4340912\n",
       "sample estimates:\n",
       "       cor \n",
       "0.07280495 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tPearson's product-moment correlation\n",
       "\n",
       "data:  relation$Y1 and relation$Y5\n",
       "t = 1.6873, df = 26, p-value = 0.1035\n",
       "alternative hypothesis: true correlation is not equal to 0\n",
       "95 percent confidence interval:\n",
       " -0.06675539  0.61512889\n",
       "sample estimates:\n",
       "      cor \n",
       "0.3141453 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tPearson's product-moment correlation\n",
       "\n",
       "data:  relation$Y1 and relation$Y6\n",
       "t = -2.0161, df = 26, p-value = 0.05423\n",
       "alternative hypothesis: true correlation is not equal to 0\n",
       "95 percent confidence interval:\n",
       " -0.651411333  0.006239047\n",
       "sample estimates:\n",
       "       cor \n",
       "-0.3676937 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tPearson's product-moment correlation\n",
       "\n",
       "data:  relation$Y1 and relation$Y7\n",
       "t = 0.22034, df = 26, p-value = 0.8273\n",
       "alternative hypothesis: true correlation is not equal to 0\n",
       "95 percent confidence interval:\n",
       " -0.3353049  0.4096515\n",
       "sample estimates:\n",
       "       cor \n",
       "0.04317269 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tPearson's product-moment correlation\n",
       "\n",
       "data:  relation$Y1 and relation$Y8\n",
       "t = 2.2233, df = 26, p-value = 0.0351\n",
       "alternative hypothesis: true correlation is not equal to 0\n",
       "95 percent confidence interval:\n",
       " 0.03126576 0.67248257\n",
       "sample estimates:\n",
       "      cor \n",
       "0.3996806 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tPearson's product-moment correlation\n",
       "\n",
       "data:  relation$Y1 and relation$Y9\n",
       "t = 1.4284, df = 26, p-value = 0.1651\n",
       "alternative hypothesis: true correlation is not equal to 0\n",
       "95 percent confidence interval:\n",
       " -0.1148885  0.5840500\n",
       "sample estimates:\n",
       "      cor \n",
       "0.2697505 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tPearson's product-moment correlation\n",
       "\n",
       "data:  relation$Y1 and relation$Y10\n",
       "t = 0.013351, df = 26, p-value = 0.9894\n",
       "alternative hypothesis: true correlation is not equal to 0\n",
       "95 percent confidence interval:\n",
       " -0.3708209  0.3753286\n",
       "sample estimates:\n",
       "        cor \n",
       "0.002618298 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tPearson's product-moment correlation\n",
       "\n",
       "data:  relation$Y1 and relation$Y11\n",
       "t = -0.48014, df = 26, p-value = 0.6351\n",
       "alternative hypothesis: true correlation is not equal to 0\n",
       "95 percent confidence interval:\n",
       " -0.4510497  0.2894523\n",
       "sample estimates:\n",
       "        cor \n",
       "-0.09374834 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tPearson's product-moment correlation\n",
       "\n",
       "data:  relation$Y2 and relation$Y3\n",
       "t = -0.5077, df = 26, p-value = 0.6159\n",
       "alternative hypothesis: true correlation is not equal to 0\n",
       "95 percent confidence interval:\n",
       " -0.4553247  0.2845153\n",
       "sample estimates:\n",
       "        cor \n",
       "-0.09907837 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tPearson's product-moment correlation\n",
       "\n",
       "data:  relation$Y2 and relation$Y4\n",
       "t = 1.1858, df = 26, p-value = 0.2464\n",
       "alternative hypothesis: true correlation is not equal to 0\n",
       "95 percent confidence interval:\n",
       " -0.1600924  0.5528691\n",
       "sample estimates:\n",
       "      cor \n",
       "0.2265135 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tPearson's product-moment correlation\n",
       "\n",
       "data:  relation$Y2 and relation$Y5\n",
       "t = 2.7097, df = 26, p-value = 0.01176\n",
       "alternative hypothesis: true correlation is not equal to 0\n",
       "95 percent confidence interval:\n",
       " 0.1166096 0.7168467\n",
       "sample estimates:\n",
       "      cor \n",
       "0.4692712 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tPearson's product-moment correlation\n",
       "\n",
       "data:  relation$Y2 and relation$Y6\n",
       "t = -1.7912, df = 26, p-value = 0.08491\n",
       "alternative hypothesis: true correlation is not equal to 0\n",
       "95 percent confidence interval:\n",
       " -0.62698322  0.04752009\n",
       "sample estimates:\n",
       "       cor \n",
       "-0.3314327 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tPearson's product-moment correlation\n",
       "\n",
       "data:  relation$Y2 and relation$Y7\n",
       "t = 0.35592, df = 26, p-value = 0.7248\n",
       "alternative hypothesis: true correlation is not equal to 0\n",
       "95 percent confidence interval:\n",
       " -0.3115373  0.4315001\n",
       "sample estimates:\n",
       "       cor \n",
       "0.06963285 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tPearson's product-moment correlation\n",
       "\n",
       "data:  relation$Y2 and relation$Y8\n",
       "t = 0.86746, df = 26, p-value = 0.3936\n",
       "alternative hypothesis: true correlation is not equal to 0\n",
       "95 percent confidence interval:\n",
       " -0.2190701  0.5089462\n",
       "sample estimates:\n",
       "      cor \n",
       "0.1677141 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tPearson's product-moment correlation\n",
       "\n",
       "data:  relation$Y2 and relation$Y9\n",
       "t = -0.56028, df = 26, p-value = 0.5801\n",
       "alternative hypothesis: true correlation is not equal to 0\n",
       "95 percent confidence interval:\n",
       " -0.4634164  0.2750623\n",
       "sample estimates:\n",
       "      cor \n",
       "-0.109223 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tPearson's product-moment correlation\n",
       "\n",
       "data:  relation$Y2 and relation$Y10\n",
       "t = 2.2109, df = 26, p-value = 0.03604\n",
       "alternative hypothesis: true correlation is not equal to 0\n",
       "95 percent confidence interval:\n",
       " 0.02904425 0.67126277\n",
       "sample estimates:\n",
       "      cor \n",
       "0.3978106 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tPearson's product-moment correlation\n",
       "\n",
       "data:  relation$Y2 and relation$Y11\n",
       "t = -1.2436, df = 26, p-value = 0.2247\n",
       "alternative hypothesis: true correlation is not equal to 0\n",
       "95 percent confidence interval:\n",
       " -0.5604747  0.1493354\n",
       "sample estimates:\n",
       "       cor \n",
       "-0.2369425 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tPearson's product-moment correlation\n",
       "\n",
       "data:  relation$Y3 and relation$Y4\n",
       "t = 1.2923, df = 26, p-value = 0.2076\n",
       "alternative hypothesis: true correlation is not equal to 0\n",
       "95 percent confidence interval:\n",
       " -0.1402566  0.5668019\n",
       "sample estimates:\n",
       "      cor \n",
       "0.2456757 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tPearson's product-moment correlation\n",
       "\n",
       "data:  relation$Y3 and relation$Y5\n",
       "t = -0.66727, df = 26, p-value = 0.5105\n",
       "alternative hypothesis: true correlation is not equal to 0\n",
       "95 percent confidence interval:\n",
       " -0.4796156  0.2556986\n",
       "sample estimates:\n",
       "       cor \n",
       "-0.1297565 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tPearson's product-moment correlation\n",
       "\n",
       "data:  relation$Y3 and relation$Y6\n",
       "t = -0.029419, df = 26, p-value = 0.9768\n",
       "alternative hypothesis: true correlation is not equal to 0\n",
       "95 percent confidence interval:\n",
       " -0.3780327  0.3680998\n",
       "sample estimates:\n",
       "         cor \n",
       "-0.005769442 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tPearson's product-moment correlation\n",
       "\n",
       "data:  relation$Y3 and relation$Y7\n",
       "t = -0.21864, df = 26, p-value = 0.8286\n",
       "alternative hypothesis: true correlation is not equal to 0\n",
       "95 percent confidence interval:\n",
       " -0.4093734  0.3356014\n",
       "sample estimates:\n",
       "        cor \n",
       "-0.04283918 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tPearson's product-moment correlation\n",
       "\n",
       "data:  relation$Y3 and relation$Y8\n",
       "t = -0.08408, df = 26, p-value = 0.9336\n",
       "alternative hypothesis: true correlation is not equal to 0\n",
       "95 percent confidence interval:\n",
       " -0.3871826  0.3587967\n",
       "sample estimates:\n",
       "        cor \n",
       "-0.01648723 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tPearson's product-moment correlation\n",
       "\n",
       "data:  relation$Y3 and relation$Y9\n",
       "t = -1.1614, df = 26, p-value = 0.256\n",
       "alternative hypothesis: true correlation is not equal to 0\n",
       "95 percent confidence interval:\n",
       " -0.5496236  0.1646318\n",
       "sample estimates:\n",
       "       cor \n",
       "-0.2220857 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tPearson's product-moment correlation\n",
       "\n",
       "data:  relation$Y3 and relation$Y10\n",
       "t = 0.13456, df = 26, p-value = 0.894\n",
       "alternative hypothesis: true correlation is not equal to 0\n",
       "95 percent confidence interval:\n",
       " -0.3501430  0.3955639\n",
       "sample estimates:\n",
       "       cor \n",
       "0.02637999 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tPearson's product-moment correlation\n",
       "\n",
       "data:  relation$Y3 and relation$Y11\n",
       "t = -1.6543, df = 26, p-value = 0.1101\n",
       "alternative hypothesis: true correlation is not equal to 0\n",
       "95 percent confidence interval:\n",
       " -0.61130196  0.07285875\n",
       "sample estimates:\n",
       "       cor \n",
       "-0.3086067 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tPearson's product-moment correlation\n",
       "\n",
       "data:  relation$Y4 and relation$Y5\n",
       "t = 0.49024, df = 26, p-value = 0.6281\n",
       "alternative hypothesis: true correlation is not equal to 0\n",
       "95 percent confidence interval:\n",
       " -0.2876439  0.4526195\n",
       "sample estimates:\n",
       "       cor \n",
       "0.09570321 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tPearson's product-moment correlation\n",
       "\n",
       "data:  relation$Y4 and relation$Y6\n",
       "t = -0.43891, df = 26, p-value = 0.6644\n",
       "alternative hypothesis: true correlation is not equal to 0\n",
       "95 percent confidence interval:\n",
       " -0.4446117  0.2968133\n",
       "sample estimates:\n",
       "        cor \n",
       "-0.08576023 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tPearson's product-moment correlation\n",
       "\n",
       "data:  relation$Y4 and relation$Y7\n",
       "t = -0.59158, df = 26, p-value = 0.5592\n",
       "alternative hypothesis: true correlation is not equal to 0\n",
       "95 percent confidence interval:\n",
       " -0.4681922  0.2694151\n",
       "sample estimates:\n",
       "       cor \n",
       "-0.1152454 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tPearson's product-moment correlation\n",
       "\n",
       "data:  relation$Y4 and relation$Y8\n",
       "t = 0.79834, df = 26, p-value = 0.4319\n",
       "alternative hypothesis: true correlation is not equal to 0\n",
       "95 percent confidence interval:\n",
       " -0.2317682  0.4989660\n",
       "sample estimates:\n",
       "      cor \n",
       "0.1546839 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tPearson's product-moment correlation\n",
       "\n",
       "data:  relation$Y4 and relation$Y9\n",
       "t = -0.64895, df = 26, p-value = 0.5221\n",
       "alternative hypothesis: true correlation is not equal to 0\n",
       "95 percent confidence interval:\n",
       " -0.4768670  0.2590262\n",
       "sample estimates:\n",
       "       cor \n",
       "-0.1262512 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tPearson's product-moment correlation\n",
       "\n",
       "data:  relation$Y4 and relation$Y10\n",
       "t = 1.7486, df = 26, p-value = 0.09216\n",
       "alternative hypothesis: true correlation is not equal to 0\n",
       "95 percent confidence interval:\n",
       " -0.05539499  0.62216755\n",
       "sample estimates:\n",
       "      cor \n",
       "0.3243859 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tPearson's product-moment correlation\n",
       "\n",
       "data:  relation$Y4 and relation$Y11\n",
       "t = -1.8238, df = 26, p-value = 0.0797\n",
       "alternative hypothesis: true correlation is not equal to 0\n",
       "95 percent confidence interval:\n",
       " -0.63062382  0.04151056\n",
       "sample estimates:\n",
       "      cor \n",
       "-0.336782 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tPearson's product-moment correlation\n",
       "\n",
       "data:  relation$Y5 and relation$Y6\n",
       "t = -0.83025, df = 26, p-value = 0.414\n",
       "alternative hypothesis: true correlation is not equal to 0\n",
       "95 percent confidence interval:\n",
       " -0.5035916  0.2259136\n",
       "sample estimates:\n",
       "       cor \n",
       "-0.1607083 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tPearson's product-moment correlation\n",
       "\n",
       "data:  relation$Y5 and relation$Y7\n",
       "t = 0.048602, df = 26, p-value = 0.9616\n",
       "alternative hypothesis: true correlation is not equal to 0\n",
       "95 percent confidence interval:\n",
       " -0.3648430  0.3812525\n",
       "sample estimates:\n",
       "        cor \n",
       "0.009531276 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tPearson's product-moment correlation\n",
       "\n",
       "data:  relation$Y5 and relation$Y8\n",
       "t = 1.4276, df = 26, p-value = 0.1653\n",
       "alternative hypothesis: true correlation is not equal to 0\n",
       "95 percent confidence interval:\n",
       " -0.1150430  0.5839468\n",
       "sample estimates:\n",
       "      cor \n",
       "0.2696053 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tPearson's product-moment correlation\n",
       "\n",
       "data:  relation$Y5 and relation$Y9\n",
       "t = 0.1897, df = 26, p-value = 0.851\n",
       "alternative hypothesis: true correlation is not equal to 0\n",
       "95 percent confidence interval:\n",
       " -0.3406234  0.4046425\n",
       "sample estimates:\n",
       "       cor \n",
       "0.03717803 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tPearson's product-moment correlation\n",
       "\n",
       "data:  relation$Y5 and relation$Y10\n",
       "t = -0.36764, df = 26, p-value = 0.7161\n",
       "alternative hypothesis: true correlation is not equal to 0\n",
       "95 percent confidence interval:\n",
       " -0.4333642  0.3094655\n",
       "sample estimates:\n",
       "        cor \n",
       "-0.07191429 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tPearson's product-moment correlation\n",
       "\n",
       "data:  relation$Y5 and relation$Y11\n",
       "t = -0.3284, df = 26, p-value = 0.7452\n",
       "alternative hypothesis: true correlation is not equal to 0\n",
       "95 percent confidence interval:\n",
       " -0.4271069  0.3163922\n",
       "sample estimates:\n",
       "        cor \n",
       "-0.06427117 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tPearson's product-moment correlation\n",
       "\n",
       "data:  relation$Y6 and relation$Y7\n",
       "t = -3.9711, df = 26, p-value = 0.0005041\n",
       "alternative hypothesis: true correlation is not equal to 0\n",
       "95 percent confidence interval:\n",
       " -0.8033582 -0.3131423\n",
       "sample estimates:\n",
       "       cor \n",
       "-0.6144369 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tPearson's product-moment correlation\n",
       "\n",
       "data:  relation$Y6 and relation$Y8\n",
       "t = 2.5973, df = 26, p-value = 0.01526\n",
       "alternative hypothesis: true correlation is not equal to 0\n",
       "95 percent confidence interval:\n",
       " 0.09727328 0.70720369\n",
       "sample estimates:\n",
       "      cor \n",
       "0.4538788 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tPearson's product-moment correlation\n",
       "\n",
       "data:  relation$Y6 and relation$Y9\n",
       "t = -0.0060965, df = 26, p-value = 0.9952\n",
       "alternative hypothesis: true correlation is not equal to 0\n",
       "95 percent confidence interval:\n",
       " -0.3741057  0.3720473\n",
       "sample estimates:\n",
       "         cor \n",
       "-0.001195629 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tPearson's product-moment correlation\n",
       "\n",
       "data:  relation$Y6 and relation$Y10\n",
       "t = -0.70264, df = 26, p-value = 0.4885\n",
       "alternative hypothesis: true correlation is not equal to 0\n",
       "95 percent confidence interval:\n",
       " -0.4848906  0.2492632\n",
       "sample estimates:\n",
       "       cor \n",
       "-0.1365082 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tPearson's product-moment correlation\n",
       "\n",
       "data:  relation$Y6 and relation$Y11\n",
       "t = 1.0574, df = 26, p-value = 0.3001\n",
       "alternative hypothesis: true correlation is not equal to 0\n",
       "95 percent confidence interval:\n",
       " -0.1839615  0.5355576\n",
       "sample estimates:\n",
       "      cor \n",
       "0.2030512 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tPearson's product-moment correlation\n",
       "\n",
       "data:  relation$Y7 and relation$Y8\n",
       "t = -2.7066, df = 26, p-value = 0.01185\n",
       "alternative hypothesis: true correlation is not equal to 0\n",
       "95 percent confidence interval:\n",
       " -0.7165831 -0.1160749\n",
       "sample estimates:\n",
       "       cor \n",
       "-0.4688484 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tPearson's product-moment correlation\n",
       "\n",
       "data:  relation$Y7 and relation$Y9\n",
       "t = -1.3994, df = 26, p-value = 0.1735\n",
       "alternative hypothesis: true correlation is not equal to 0\n",
       "95 percent confidence interval:\n",
       " -0.5804276  0.1202929\n",
       "sample estimates:\n",
       "       cor \n",
       "-0.2646616 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tPearson's product-moment correlation\n",
       "\n",
       "data:  relation$Y7 and relation$Y10\n",
       "t = 0.32645, df = 26, p-value = 0.7467\n",
       "alternative hypothesis: true correlation is not equal to 0\n",
       "95 percent confidence interval:\n",
       " -0.3167360  0.4267945\n",
       "sample estimates:\n",
       "       cor \n",
       "0.06389073 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tPearson's product-moment correlation\n",
       "\n",
       "data:  relation$Y7 and relation$Y11\n",
       "t = -0.28724, df = 26, p-value = 0.7762\n",
       "alternative hypothesis: true correlation is not equal to 0\n",
       "95 percent confidence interval:\n",
       " -0.4204961  0.3236252\n",
       "sample estimates:\n",
       "        cor \n",
       "-0.05624227 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tPearson's product-moment correlation\n",
       "\n",
       "data:  relation$Y8 and relation$Y9\n",
       "t = -0.058966, df = 26, p-value = 0.9534\n",
       "alternative hypothesis: true correlation is not equal to 0\n",
       "95 percent confidence interval:\n",
       " -0.3829881  0.3630799\n",
       "sample estimates:\n",
       "        cor \n",
       "-0.01156338 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tPearson's product-moment correlation\n",
       "\n",
       "data:  relation$Y8 and relation$Y10\n",
       "t = -0.96936, df = 26, p-value = 0.3413\n",
       "alternative hypothesis: true correlation is not equal to 0\n",
       "95 percent confidence interval:\n",
       " -0.5233726  0.2002684\n",
       "sample estimates:\n",
       "       cor \n",
       "-0.1867626 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tPearson's product-moment correlation\n",
       "\n",
       "data:  relation$Y8 and relation$Y11\n",
       "t = -0.20713, df = 26, p-value = 0.8375\n",
       "alternative hypothesis: true correlation is not equal to 0\n",
       "95 percent confidence interval:\n",
       " -0.4074949  0.3376005\n",
       "sample estimates:\n",
       "        cor \n",
       "-0.04058856 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tPearson's product-moment correlation\n",
       "\n",
       "data:  relation$Y9 and relation$Y10\n",
       "t = 0.58027, df = 26, p-value = 0.5667\n",
       "alternative hypothesis: true correlation is not equal to 0\n",
       "95 percent confidence interval:\n",
       " -0.2714573  0.4664701\n",
       "sample estimates:\n",
       "      cor \n",
       "0.1130708 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tPearson's product-moment correlation\n",
       "\n",
       "data:  relation$Y9 and relation$Y11\n",
       "t = 0.53731, df = 26, p-value = 0.5956\n",
       "alternative hypothesis: true correlation is not equal to 0\n",
       "95 percent confidence interval:\n",
       " -0.2791975  0.4598918\n",
       "sample estimates:\n",
       "      cor \n",
       "0.1047951 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\tPearson's product-moment correlation\n",
       "\n",
       "data:  relation$Y10 and relation$Y11\n",
       "t = -2.0247, df = 26, p-value = 0.05328\n",
       "alternative hypothesis: true correlation is not equal to 0\n",
       "95 percent confidence interval:\n",
       " -0.652309740  0.004676858\n",
       "sample estimates:\n",
       "      cor \n",
       "-0.369044 \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Y1～11\n",
    "cor.test(relation$Y1, relation$Y2,   method = \"pearson\")\n",
    "cor.test(relation$Y1, relation$Y3,   method = \"pearson\")\n",
    "cor.test(relation$Y1, relation$Y4,   method = \"pearson\") \n",
    "cor.test(relation$Y1, relation$Y5,   method = \"pearson\")  \n",
    "cor.test(relation$Y1, relation$Y6,   method = \"pearson\")  \n",
    "cor.test(relation$Y1, relation$Y7,   method = \"pearson\")  \n",
    "cor.test(relation$Y1, relation$Y8,   method = \"pearson\")  \n",
    "cor.test(relation$Y1, relation$Y9,   method = \"pearson\")  \n",
    "cor.test(relation$Y1, relation$Y10,  method = \"pearson\")  \n",
    "cor.test(relation$Y1, relation$Y11,  method = \"pearson\") \n",
    "#Y2～11\n",
    "cor.test(relation$Y2, relation$Y3,   method = \"pearson\")  \n",
    "cor.test(relation$Y2, relation$Y4,   method = \"pearson\")  \n",
    "cor.test(relation$Y2, relation$Y5,   method = \"pearson\")  \n",
    "cor.test(relation$Y2, relation$Y6,   method = \"pearson\")  \n",
    "cor.test(relation$Y2, relation$Y7,   method = \"pearson\")  \n",
    "cor.test(relation$Y2, relation$Y8,   method = \"pearson\")  \n",
    "cor.test(relation$Y2, relation$Y9,   method = \"pearson\")  \n",
    "cor.test(relation$Y2, relation$Y10,  method = \"pearson\")  \n",
    "cor.test(relation$Y2, relation$Y11,  method = \"pearson\")\n",
    "#Y3～11\n",
    "cor.test(relation$Y3, relation$Y4,   method = \"pearson\")  \n",
    "cor.test(relation$Y3, relation$Y5,   method = \"pearson\")  \n",
    "cor.test(relation$Y3, relation$Y6,   method = \"pearson\")  \n",
    "cor.test(relation$Y3, relation$Y7,   method = \"pearson\")  \n",
    "cor.test(relation$Y3, relation$Y8,   method = \"pearson\")  \n",
    "cor.test(relation$Y3, relation$Y9,   method = \"pearson\")  \n",
    "cor.test(relation$Y3, relation$Y10,  method = \"pearson\")  \n",
    "cor.test(relation$Y3, relation$Y11,  method = \"pearson\")\n",
    "#Y4～11\n",
    "cor.test(relation$Y4, relation$Y5,   method = \"pearson\")  \n",
    "cor.test(relation$Y4, relation$Y6,   method = \"pearson\")  \n",
    "cor.test(relation$Y4, relation$Y7,   method = \"pearson\")  \n",
    "cor.test(relation$Y4, relation$Y8,   method = \"pearson\")  \n",
    "cor.test(relation$Y4, relation$Y9,   method = \"pearson\")  \n",
    "cor.test(relation$Y4, relation$Y10,  method = \"pearson\")  \n",
    "cor.test(relation$Y4, relation$Y11,  method = \"pearson\")\n",
    "#Y5～11\n",
    "cor.test(relation$Y5, relation$Y6,   method = \"pearson\")  \n",
    "cor.test(relation$Y5, relation$Y7,   method = \"pearson\")  \n",
    "cor.test(relation$Y5, relation$Y8,   method = \"pearson\")  \n",
    "cor.test(relation$Y5, relation$Y9,   method = \"pearson\")  \n",
    "cor.test(relation$Y5, relation$Y10,  method = \"pearson\")  \n",
    "cor.test(relation$Y5, relation$Y11,  method = \"pearson\")\n",
    "#Y6～11\n",
    "cor.test(relation$Y6, relation$Y7,   method = \"pearson\")  \n",
    "cor.test(relation$Y6, relation$Y8,   method = \"pearson\")  \n",
    "cor.test(relation$Y6, relation$Y9,   method = \"pearson\")  \n",
    "cor.test(relation$Y6, relation$Y10,  method = \"pearson\")  \n",
    "cor.test(relation$Y6, relation$Y11,  method = \"pearson\")\n",
    "#Y7～11\n",
    "cor.test(relation$Y7, relation$Y8,   method = \"pearson\")  \n",
    "cor.test(relation$Y7, relation$Y9,   method = \"pearson\")  \n",
    "cor.test(relation$Y7, relation$Y10,  method = \"pearson\")  \n",
    "cor.test(relation$Y7, relation$Y11,  method = \"pearson\")\n",
    "#Y8～11\n",
    "cor.test(relation$Y8, relation$Y9,   method = \"pearson\")  \n",
    "cor.test(relation$Y8, relation$Y10,  method = \"pearson\")  \n",
    "cor.test(relation$Y8, relation$Y11,  method = \"pearson\")\n",
    "#Y9～11\n",
    "cor.test(relation$Y9, relation$Y10,  method = \"pearson\")  \n",
    "cor.test(relation$Y9, relation$Y11,  method = \"pearson\")\n",
    "#Y10～11\n",
    "cor.test(relation$Y10,relation$Y11,  method = \"pearson\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 多変量時系列分析\n",
    "- 単位根検定(ADF検定)\n",
    "- 共和分検定\n",
    "- 偏グレンジャー因果性検定と非直交化インパルス応答関数\n",
    "- パネルVARモデルによる動的直接相関係数の導出\n",
    "- 幾何ブラウン運動と確率偏微分方程式\n",
    "- LSTM(Long Short Term Memory)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 単位根検定(ADF検定)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "$Y1\n",
       "\n",
       "############################################### \n",
       "# Augmented Dickey-Fuller Test Unit Root Test # \n",
       "############################################### \n",
       "\n",
       "Test regression drift \n",
       "\n",
       "\n",
       "Call:\n",
       "lm(formula = z.diff ~ z.lag.1 + 1 + z.diff.lag)\n",
       "\n",
       "Residuals:\n",
       "     Min       1Q   Median       3Q      Max \n",
       "-0.06802 -0.01276  0.00237  0.01493  0.05123 \n",
       "\n",
       "Coefficients:\n",
       "              Estimate Std. Error t value Pr(>|t|)   \n",
       "(Intercept)  0.0004843  0.0052101   0.093  0.92674   \n",
       "z.lag.1     -0.6311647  0.2209387  -2.857  0.00892 **\n",
       "z.diff.lag  -0.0450070  0.1992158  -0.226  0.82326   \n",
       "---\n",
       "Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
       "\n",
       "Residual standard error: 0.02523 on 23 degrees of freedom\n",
       "Multiple R-squared:  0.3465,\tAdjusted R-squared:  0.2897 \n",
       "F-statistic: 6.098 on 2 and 23 DF,  p-value: 0.0075\n",
       "\n",
       "\n",
       "Value of test-statistic is: -2.8567 4.4176 \n",
       "\n",
       "Critical values for test statistics: \n",
       "      1pct  5pct 10pct\n",
       "tau2 -3.58 -2.93 -2.60\n",
       "phi1  7.06  4.86  3.94\n",
       "\n",
       "\n",
       "$Y2\n",
       "\n",
       "############################################### \n",
       "# Augmented Dickey-Fuller Test Unit Root Test # \n",
       "############################################### \n",
       "\n",
       "Test regression drift \n",
       "\n",
       "\n",
       "Call:\n",
       "lm(formula = z.diff ~ z.lag.1 + 1 + z.diff.lag)\n",
       "\n",
       "Residuals:\n",
       "      Min        1Q    Median        3Q       Max \n",
       "-0.120279 -0.012060  0.003403  0.018147  0.074868 \n",
       "\n",
       "Coefficients:\n",
       "             Estimate Std. Error t value Pr(>|t|)   \n",
       "(Intercept)  0.003849   0.008635   0.446  0.65996   \n",
       "z.lag.1     -1.077094   0.329024  -3.274  0.00334 **\n",
       "z.diff.lag   0.173688   0.252811   0.687  0.49894   \n",
       "---\n",
       "Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
       "\n",
       "Residual standard error: 0.04106 on 23 degrees of freedom\n",
       "Multiple R-squared:  0.4724,\tAdjusted R-squared:  0.4265 \n",
       "F-statistic:  10.3 on 2 and 23 DF,  p-value: 0.0006405\n",
       "\n",
       "\n",
       "Value of test-statistic is: -3.2736 5.6437 \n",
       "\n",
       "Critical values for test statistics: \n",
       "      1pct  5pct 10pct\n",
       "tau2 -3.58 -2.93 -2.60\n",
       "phi1  7.06  4.86  3.94\n",
       "\n",
       "\n",
       "$Y3\n",
       "\n",
       "############################################### \n",
       "# Augmented Dickey-Fuller Test Unit Root Test # \n",
       "############################################### \n",
       "\n",
       "Test regression drift \n",
       "\n",
       "\n",
       "Call:\n",
       "lm(formula = z.diff ~ z.lag.1 + 1 + z.diff.lag)\n",
       "\n",
       "Residuals:\n",
       "     Min       1Q   Median       3Q      Max \n",
       "-0.50000  0.00000  0.05789  0.18421  0.31579 \n",
       "\n",
       "Coefficients:\n",
       "            Estimate Std. Error t value Pr(>|t|)    \n",
       "(Intercept)  0.00000    0.04929   0.000  1.00000    \n",
       "z.lag.1     -2.40000    0.31789  -7.550 1.14e-07 ***\n",
       "z.diff.lag   0.51579    0.17864   2.887  0.00831 ** \n",
       "---\n",
       "Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
       "\n",
       "Residual standard error: 0.2513 on 23 degrees of freedom\n",
       "Multiple R-squared:  0.8471,\tAdjusted R-squared:  0.8338 \n",
       "F-statistic: 63.71 on 2 and 23 DF,  p-value: 4.178e-10\n",
       "\n",
       "\n",
       "Value of test-statistic is: -7.5498 28.5 \n",
       "\n",
       "Critical values for test statistics: \n",
       "      1pct  5pct 10pct\n",
       "tau2 -3.58 -2.93 -2.60\n",
       "phi1  7.06  4.86  3.94\n",
       "\n",
       "\n",
       "$Y4\n",
       "\n",
       "############################################### \n",
       "# Augmented Dickey-Fuller Test Unit Root Test # \n",
       "############################################### \n",
       "\n",
       "Test regression drift \n",
       "\n",
       "\n",
       "Call:\n",
       "lm(formula = z.diff ~ z.lag.1 + 1 + z.diff.lag)\n",
       "\n",
       "Residuals:\n",
       "     Min       1Q   Median       3Q      Max \n",
       "-0.42834 -0.12795  0.00796  0.13422  0.31986 \n",
       "\n",
       "Coefficients:\n",
       "            Estimate Std. Error t value Pr(>|t|)    \n",
       "(Intercept)  0.08524    0.04169   2.045   0.0525 .  \n",
       "z.lag.1     -1.43324    0.28552  -5.020 4.44e-05 ***\n",
       "z.diff.lag   0.29721    0.18973   1.566   0.1309    \n",
       "---\n",
       "Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
       "\n",
       "Residual standard error: 0.1966 on 23 degrees of freedom\n",
       "Multiple R-squared:  0.6125,\tAdjusted R-squared:  0.5788 \n",
       "F-statistic: 18.17 on 2 and 23 DF,  p-value: 1.843e-05\n",
       "\n",
       "\n",
       "Value of test-statistic is: -5.0198 12.6099 \n",
       "\n",
       "Critical values for test statistics: \n",
       "      1pct  5pct 10pct\n",
       "tau2 -3.58 -2.93 -2.60\n",
       "phi1  7.06  4.86  3.94\n",
       "\n",
       "\n",
       "$Y5\n",
       "\n",
       "############################################### \n",
       "# Augmented Dickey-Fuller Test Unit Root Test # \n",
       "############################################### \n",
       "\n",
       "Test regression drift \n",
       "\n",
       "\n",
       "Call:\n",
       "lm(formula = z.diff ~ z.lag.1 + 1 + z.diff.lag)\n",
       "\n",
       "Residuals:\n",
       "      Min        1Q    Median        3Q       Max \n",
       "-0.171472 -0.027863 -0.001165  0.064972  0.105679 \n",
       "\n",
       "Coefficients:\n",
       "            Estimate Std. Error t value Pr(>|t|)    \n",
       "(Intercept)  0.02027    0.01703   1.190 0.246262    \n",
       "z.lag.1     -1.03584    0.22304  -4.644 0.000113 ***\n",
       "z.diff.lag   0.40543    0.19557   2.073 0.049558 *  \n",
       "---\n",
       "Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
       "\n",
       "Residual standard error: 0.07872 on 23 degrees of freedom\n",
       "Multiple R-squared:  0.4967,\tAdjusted R-squared:  0.453 \n",
       "F-statistic: 11.35 on 2 and 23 DF,  p-value: 0.0003721\n",
       "\n",
       "\n",
       "Value of test-statistic is: -4.6442 11.0683 \n",
       "\n",
       "Critical values for test statistics: \n",
       "      1pct  5pct 10pct\n",
       "tau2 -3.58 -2.93 -2.60\n",
       "phi1  7.06  4.86  3.94\n",
       "\n",
       "\n",
       "$Y6\n",
       "\n",
       "############################################### \n",
       "# Augmented Dickey-Fuller Test Unit Root Test # \n",
       "############################################### \n",
       "\n",
       "Test regression drift \n",
       "\n",
       "\n",
       "Call:\n",
       "lm(formula = z.diff ~ z.lag.1 + 1 + z.diff.lag)\n",
       "\n",
       "Residuals:\n",
       "      Min        1Q    Median        3Q       Max \n",
       "-0.044331 -0.021287 -0.000129  0.009790  0.100182 \n",
       "\n",
       "Coefficients:\n",
       "            Estimate Std. Error t value Pr(>|t|)  \n",
       "(Intercept)  0.01887    0.00998   1.890   0.0714 .\n",
       "z.lag.1     -0.34762    0.15133  -2.297   0.0311 *\n",
       "z.diff.lag   0.02227    0.19728   0.113   0.9111  \n",
       "---\n",
       "Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
       "\n",
       "Residual standard error: 0.0353 on 23 degrees of freedom\n",
       "Multiple R-squared:  0.202,\tAdjusted R-squared:  0.1326 \n",
       "F-statistic: 2.911 on 2 and 23 DF,  p-value: 0.07464\n",
       "\n",
       "\n",
       "Value of test-statistic is: -2.297 2.6965 \n",
       "\n",
       "Critical values for test statistics: \n",
       "      1pct  5pct 10pct\n",
       "tau2 -3.58 -2.93 -2.60\n",
       "phi1  7.06  4.86  3.94\n",
       "\n",
       "\n",
       "$Y7\n",
       "\n",
       "############################################### \n",
       "# Augmented Dickey-Fuller Test Unit Root Test # \n",
       "############################################### \n",
       "\n",
       "Test regression drift \n",
       "\n",
       "\n",
       "Call:\n",
       "lm(formula = z.diff ~ z.lag.1 + 1 + z.diff.lag)\n",
       "\n",
       "Residuals:\n",
       "       Min         1Q     Median         3Q        Max \n",
       "-0.0269513 -0.0061571 -0.0009426  0.0027593  0.0210403 \n",
       "\n",
       "Coefficients:\n",
       "             Estimate Std. Error t value Pr(>|t|)  \n",
       "(Intercept)  0.002465   0.002223   1.109   0.2788  \n",
       "z.lag.1     -0.382387   0.192007  -1.992   0.0584 .\n",
       "z.diff.lag   0.094636   0.228577   0.414   0.6827  \n",
       "---\n",
       "Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
       "\n",
       "Residual standard error: 0.01047 on 23 degrees of freedom\n",
       "Multiple R-squared:  0.1537,\tAdjusted R-squared:  0.08006 \n",
       "F-statistic: 2.088 on 2 and 23 DF,  p-value: 0.1468\n",
       "\n",
       "\n",
       "Value of test-statistic is: -1.9915 2.0532 \n",
       "\n",
       "Critical values for test statistics: \n",
       "      1pct  5pct 10pct\n",
       "tau2 -3.58 -2.93 -2.60\n",
       "phi1  7.06  4.86  3.94\n",
       "\n",
       "\n",
       "$Y8\n",
       "\n",
       "############################################### \n",
       "# Augmented Dickey-Fuller Test Unit Root Test # \n",
       "############################################### \n",
       "\n",
       "Test regression drift \n",
       "\n",
       "\n",
       "Call:\n",
       "lm(formula = z.diff ~ z.lag.1 + 1 + z.diff.lag)\n",
       "\n",
       "Residuals:\n",
       "     Min       1Q   Median       3Q      Max \n",
       "-0.15411 -0.05724 -0.00855  0.02925  0.20514 \n",
       "\n",
       "Coefficients:\n",
       "             Estimate Std. Error t value Pr(>|t|)   \n",
       "(Intercept)  0.009094   0.017567   0.518  0.60962   \n",
       "z.lag.1     -0.634513   0.208216  -3.047  0.00572 **\n",
       "z.diff.lag   0.258335   0.203640   1.269  0.21728   \n",
       "---\n",
       "Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
       "\n",
       "Residual standard error: 0.08707 on 23 degrees of freedom\n",
       "Multiple R-squared:  0.2907,\tAdjusted R-squared:  0.229 \n",
       "F-statistic: 4.712 on 2 and 23 DF,  p-value: 0.01927\n",
       "\n",
       "\n",
       "Value of test-statistic is: -3.0474 4.66 \n",
       "\n",
       "Critical values for test statistics: \n",
       "      1pct  5pct 10pct\n",
       "tau2 -3.58 -2.93 -2.60\n",
       "phi1  7.06  4.86  3.94\n",
       "\n",
       "\n",
       "$Y9\n",
       "\n",
       "############################################### \n",
       "# Augmented Dickey-Fuller Test Unit Root Test # \n",
       "############################################### \n",
       "\n",
       "Test regression drift \n",
       "\n",
       "\n",
       "Call:\n",
       "lm(formula = z.diff ~ z.lag.1 + 1 + z.diff.lag)\n",
       "\n",
       "Residuals:\n",
       "     Min       1Q   Median       3Q      Max \n",
       "-0.51030 -0.14007  0.03693  0.18080  0.42540 \n",
       "\n",
       "Coefficients:\n",
       "            Estimate Std. Error t value Pr(>|t|)    \n",
       "(Intercept) -0.03088    0.05145  -0.600 0.554200    \n",
       "z.lag.1     -1.14407    0.27811  -4.114 0.000424 ***\n",
       "z.diff.lag   0.20340    0.20929   0.972 0.341234    \n",
       "---\n",
       "Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
       "\n",
       "Residual standard error: 0.2604 on 23 degrees of freedom\n",
       "Multiple R-squared:  0.5142,\tAdjusted R-squared:  0.4719 \n",
       "F-statistic: 12.17 on 2 and 23 DF,  p-value: 0.0002481\n",
       "\n",
       "\n",
       "Value of test-statistic is: -4.1137 8.4689 \n",
       "\n",
       "Critical values for test statistics: \n",
       "      1pct  5pct 10pct\n",
       "tau2 -3.58 -2.93 -2.60\n",
       "phi1  7.06  4.86  3.94\n",
       "\n",
       "\n",
       "$Y10\n",
       "\n",
       "############################################### \n",
       "# Augmented Dickey-Fuller Test Unit Root Test # \n",
       "############################################### \n",
       "\n",
       "Test regression drift \n",
       "\n",
       "\n",
       "Call:\n",
       "lm(formula = z.diff ~ z.lag.1 + 1 + z.diff.lag)\n",
       "\n",
       "Residuals:\n",
       "     Min       1Q   Median       3Q      Max \n",
       "-0.91292 -0.00189 -0.00169  0.00027  0.90275 \n",
       "\n",
       "Coefficients:\n",
       "             Estimate Std. Error t value Pr(>|t|)   \n",
       "(Intercept)  0.001692   0.054196   0.031  0.97536   \n",
       "z.lag.1     -0.987538   0.294867  -3.349  0.00278 **\n",
       "z.diff.lag  -0.012505   0.208498  -0.060  0.95269   \n",
       "---\n",
       "Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
       "\n",
       "Residual standard error: 0.2763 on 23 degrees of freedom\n",
       "Multiple R-squared:  0.5001,\tAdjusted R-squared:  0.4566 \n",
       "F-statistic:  11.5 on 2 and 23 DF,  p-value: 0.0003445\n",
       "\n",
       "\n",
       "Value of test-statistic is: -3.3491 5.6082 \n",
       "\n",
       "Critical values for test statistics: \n",
       "      1pct  5pct 10pct\n",
       "tau2 -3.58 -2.93 -2.60\n",
       "phi1  7.06  4.86  3.94\n",
       "\n",
       "\n",
       "$Y11\n",
       "\n",
       "############################################### \n",
       "# Augmented Dickey-Fuller Test Unit Root Test # \n",
       "############################################### \n",
       "\n",
       "Test regression drift \n",
       "\n",
       "\n",
       "Call:\n",
       "lm(formula = z.diff ~ z.lag.1 + 1 + z.diff.lag)\n",
       "\n",
       "Residuals:\n",
       "     Min       1Q   Median       3Q      Max \n",
       "-0.46569 -0.14461  0.03431  0.16850  0.35539 \n",
       "\n",
       "Coefficients:\n",
       "            Estimate Std. Error t value Pr(>|t|)    \n",
       "(Intercept) -0.03431    0.04619  -0.743   0.4651    \n",
       "z.lag.1     -2.78431    0.33307  -8.360    2e-08 ***\n",
       "z.diff.lag   0.64216    0.18077   3.552   0.0017 ** \n",
       "---\n",
       "Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
       "\n",
       "Residual standard error: 0.2332 on 23 degrees of freedom\n",
       "Multiple R-squared:  0.8887,\tAdjusted R-squared:  0.879 \n",
       "F-statistic: 91.81 on 2 and 23 DF,  p-value: 1.085e-11\n",
       "\n",
       "\n",
       "Value of test-statistic is: -8.3595 35.0294 \n",
       "\n",
       "Critical values for test statistics: \n",
       "      1pct  5pct 10pct\n",
       "tau2 -3.58 -2.93 -2.60\n",
       "phi1  7.06  4.86  3.94\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "relation %>%\n",
    "  apply(2, ADF)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 共和分検定"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Response: relation$Y1 \n",
      "Input: relation$Y2 \n",
      "Number of inputs: 1 \n",
      "Model: y ~ X + 1 \n",
      "------------------------------- \n",
      "Engle-Granger Cointegration Test \n",
      "alternative: cointegrated \n",
      "\n",
      "Type 1: no trend \n",
      "    lag      EG p.value \n",
      "   1.00   -4.55    0.01 \n",
      "----- \n",
      " Type 2: linear trend \n",
      "    lag      EG p.value \n",
      "   1.00   -1.44    0.10 \n",
      "----- \n",
      " Type 3: quadratic trend \n",
      "    lag      EG p.value \n",
      "  1.000   0.826   0.100 \n",
      "----------- \n",
      "Note: p.value = 0.01 means p.value <= 0.01 \n",
      "    : p.value = 0.10 means p.value >= 0.10 \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "      lag          EG             p.value     \n",
       " Min.   :1   Min.   :-4.5544   Min.   :0.010  \n",
       " 1st Qu.:1   1st Qu.:-2.9967   1st Qu.:0.055  \n",
       " Median :1   Median :-1.4389   Median :0.100  \n",
       " Mean   :1   Mean   :-1.7225   Mean   :0.070  \n",
       " 3rd Qu.:1   3rd Qu.:-0.3065   3rd Qu.:0.100  \n",
       " Max.   :1   Max.   : 0.8259   Max.   :0.100  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Response: relation$Y1 \n",
      "Input: relation$Y3 \n",
      "Number of inputs: 1 \n",
      "Model: y ~ X + 1 \n",
      "------------------------------- \n",
      "Engle-Granger Cointegration Test \n",
      "alternative: cointegrated \n",
      "\n",
      "Type 1: no trend \n",
      "    lag      EG p.value \n",
      " 1.0000 -3.3169  0.0308 \n",
      "----- \n",
      " Type 2: linear trend \n",
      "    lag      EG p.value \n",
      "   1.00   -1.54    0.10 \n",
      "----- \n",
      " Type 3: quadratic trend \n",
      "    lag      EG p.value \n",
      "   1.00    1.04    0.10 \n",
      "----------- \n",
      "Note: p.value = 0.01 means p.value <= 0.01 \n",
      "    : p.value = 0.10 means p.value >= 0.10 \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "      lag          EG             p.value       \n",
       " Min.   :1   Min.   :-3.3169   Min.   :0.03076  \n",
       " 1st Qu.:1   1st Qu.:-2.4277   1st Qu.:0.06538  \n",
       " Median :1   Median :-1.5385   Median :0.10000  \n",
       " Mean   :1   Mean   :-1.2721   Mean   :0.07692  \n",
       " 3rd Qu.:1   3rd Qu.:-0.2497   3rd Qu.:0.10000  \n",
       " Max.   :1   Max.   : 1.0391   Max.   :0.10000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Response: relation$Y1 \n",
      "Input: relation$Y4 \n",
      "Number of inputs: 1 \n",
      "Model: y ~ X + 1 \n",
      "------------------------------- \n",
      "Engle-Granger Cointegration Test \n",
      "alternative: cointegrated \n",
      "\n",
      "Type 1: no trend \n",
      "    lag      EG p.value \n",
      " 1.0000 -3.2421  0.0349 \n",
      "----- \n",
      " Type 2: linear trend \n",
      "    lag      EG p.value \n",
      "   1.00   -1.52    0.10 \n",
      "----- \n",
      " Type 3: quadratic trend \n",
      "    lag      EG p.value \n",
      "  1.000   0.974   0.100 \n",
      "----------- \n",
      "Note: p.value = 0.01 means p.value <= 0.01 \n",
      "    : p.value = 0.10 means p.value >= 0.10 \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "      lag          EG             p.value       \n",
       " Min.   :1   Min.   :-3.2421   Min.   :0.03493  \n",
       " 1st Qu.:1   1st Qu.:-2.3821   1st Qu.:0.06747  \n",
       " Median :1   Median :-1.5221   Median :0.10000  \n",
       " Mean   :1   Mean   :-1.2635   Mean   :0.07831  \n",
       " 3rd Qu.:1   3rd Qu.:-0.2742   3rd Qu.:0.10000  \n",
       " Max.   :1   Max.   : 0.9737   Max.   :0.10000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Response: relation$Y1 \n",
      "Input: relation$Y5 \n",
      "Number of inputs: 1 \n",
      "Model: y ~ X + 1 \n",
      "------------------------------- \n",
      "Engle-Granger Cointegration Test \n",
      "alternative: cointegrated \n",
      "\n",
      "Type 1: no trend \n",
      "    lag      EG p.value \n",
      " 1.0000 -3.6274  0.0134 \n",
      "----- \n",
      " Type 2: linear trend \n",
      "    lag      EG p.value \n",
      "   1.00   -1.36    0.10 \n",
      "----- \n",
      " Type 3: quadratic trend \n",
      "    lag      EG p.value \n",
      "   1.00    1.04    0.10 \n",
      "----------- \n",
      "Note: p.value = 0.01 means p.value <= 0.01 \n",
      "    : p.value = 0.10 means p.value >= 0.10 \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "      lag          EG             p.value       \n",
       " Min.   :1   Min.   :-3.6274   Min.   :0.01344  \n",
       " 1st Qu.:1   1st Qu.:-2.4913   1st Qu.:0.05672  \n",
       " Median :1   Median :-1.3552   Median :0.10000  \n",
       " Mean   :1   Mean   :-1.3128   Mean   :0.07115  \n",
       " 3rd Qu.:1   3rd Qu.:-0.1555   3rd Qu.:0.10000  \n",
       " Max.   :1   Max.   : 1.0442   Max.   :0.10000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Response: relation$Y1 \n",
      "Input: relation$Y6 \n",
      "Number of inputs: 1 \n",
      "Model: y ~ X + 1 \n",
      "------------------------------- \n",
      "Engle-Granger Cointegration Test \n",
      "alternative: cointegrated \n",
      "\n",
      "Type 1: no trend \n",
      "    lag      EG p.value \n",
      " 1.0000 -3.3000  0.0317 \n",
      "----- \n",
      " Type 2: linear trend \n",
      "    lag      EG p.value \n",
      "   1.00   -1.34    0.10 \n",
      "----- \n",
      " Type 3: quadratic trend \n",
      "    lag      EG p.value \n",
      "   1.00    1.71    0.10 \n",
      "----------- \n",
      "Note: p.value = 0.01 means p.value <= 0.01 \n",
      "    : p.value = 0.10 means p.value >= 0.10 \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "      lag          EG             p.value       \n",
       " Min.   :1   Min.   :-3.3000   Min.   :0.03170  \n",
       " 1st Qu.:1   1st Qu.:-2.3197   1st Qu.:0.06585  \n",
       " Median :1   Median :-1.3394   Median :0.10000  \n",
       " Mean   :1   Mean   :-0.9764   Mean   :0.07723  \n",
       " 3rd Qu.:1   3rd Qu.: 0.1854   3rd Qu.:0.10000  \n",
       " Max.   :1   Max.   : 1.7101   Max.   :0.10000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Response: relation$Y1 \n",
      "Input: relation$Y7 \n",
      "Number of inputs: 1 \n",
      "Model: y ~ X + 1 \n",
      "------------------------------- \n",
      "Engle-Granger Cointegration Test \n",
      "alternative: cointegrated \n",
      "\n",
      "Type 1: no trend \n",
      "    lag      EG p.value \n",
      " 1.0000 -3.3616  0.0283 \n",
      "----- \n",
      " Type 2: linear trend \n",
      "    lag      EG p.value \n",
      "   1.00   -1.52    0.10 \n",
      "----- \n",
      " Type 3: quadratic trend \n",
      "    lag      EG p.value \n",
      "   1.00    1.07    0.10 \n",
      "----------- \n",
      "Note: p.value = 0.01 means p.value <= 0.01 \n",
      "    : p.value = 0.10 means p.value >= 0.10 \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "      lag          EG             p.value       \n",
       " Min.   :1   Min.   :-3.3616   Min.   :0.02826  \n",
       " 1st Qu.:1   1st Qu.:-2.4391   1st Qu.:0.06413  \n",
       " Median :1   Median :-1.5166   Median :0.10000  \n",
       " Mean   :1   Mean   :-1.2683   Mean   :0.07609  \n",
       " 3rd Qu.:1   3rd Qu.:-0.2216   3rd Qu.:0.10000  \n",
       " Max.   :1   Max.   : 1.0733   Max.   :0.10000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Response: relation$Y1 \n",
      "Input: relation$Y8 \n",
      "Number of inputs: 1 \n",
      "Model: y ~ X + 1 \n",
      "------------------------------- \n",
      "Engle-Granger Cointegration Test \n",
      "alternative: cointegrated \n",
      "\n",
      "Type 1: no trend \n",
      "    lag      EG p.value \n",
      " 1.0000 -2.7384  0.0837 \n",
      "----- \n",
      " Type 2: linear trend \n",
      "    lag      EG p.value \n",
      "   1.00   -1.33    0.10 \n",
      "----- \n",
      " Type 3: quadratic trend \n",
      "    lag      EG p.value \n",
      "  1.000   0.282   0.100 \n",
      "----------- \n",
      "Note: p.value = 0.01 means p.value <= 0.01 \n",
      "    : p.value = 0.10 means p.value >= 0.10 \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "      lag          EG             p.value       \n",
       " Min.   :1   Min.   :-2.7384   Min.   :0.08369  \n",
       " 1st Qu.:1   1st Qu.:-2.0361   1st Qu.:0.09184  \n",
       " Median :1   Median :-1.3337   Median :0.10000  \n",
       " Mean   :1   Mean   :-1.2634   Mean   :0.09456  \n",
       " 3rd Qu.:1   3rd Qu.:-0.5258   3rd Qu.:0.10000  \n",
       " Max.   :1   Max.   : 0.2820   Max.   :0.10000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Response: relation$Y1 \n",
      "Input: relation$Y9 \n",
      "Number of inputs: 1 \n",
      "Model: y ~ X + 1 \n",
      "------------------------------- \n",
      "Engle-Granger Cointegration Test \n",
      "alternative: cointegrated \n",
      "\n",
      "Type 1: no trend \n",
      "    lag      EG p.value \n",
      " 1.0000 -3.1780  0.0385 \n",
      "----- \n",
      " Type 2: linear trend \n",
      "    lag      EG p.value \n",
      "   1.00   -1.63    0.10 \n",
      "----- \n",
      " Type 3: quadratic trend \n",
      "    lag      EG p.value \n",
      "   1.00    1.48    0.10 \n",
      "----------- \n",
      "Note: p.value = 0.01 means p.value <= 0.01 \n",
      "    : p.value = 0.10 means p.value >= 0.10 \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "      lag          EG              p.value       \n",
       " Min.   :1   Min.   :-3.17803   Min.   :0.03850  \n",
       " 1st Qu.:1   1st Qu.:-2.40490   1st Qu.:0.06925  \n",
       " Median :1   Median :-1.63176   Median :0.10000  \n",
       " Mean   :1   Mean   :-1.11147   Mean   :0.07950  \n",
       " 3rd Qu.:1   3rd Qu.:-0.07819   3rd Qu.:0.10000  \n",
       " Max.   :1   Max.   : 1.47539   Max.   :0.10000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Response: relation$Y1 \n",
      "Input: relation$Y10 \n",
      "Number of inputs: 1 \n",
      "Model: y ~ X + 1 \n",
      "------------------------------- \n",
      "Engle-Granger Cointegration Test \n",
      "alternative: cointegrated \n",
      "\n",
      "Type 1: no trend \n",
      "    lag      EG p.value \n",
      " 1.0000 -3.2911  0.0322 \n",
      "----- \n",
      " Type 2: linear trend \n",
      "    lag      EG p.value \n",
      "   1.00   -1.52    0.10 \n",
      "----- \n",
      " Type 3: quadratic trend \n",
      "    lag      EG p.value \n",
      "   1.00    1.02    0.10 \n",
      "----------- \n",
      "Note: p.value = 0.01 means p.value <= 0.01 \n",
      "    : p.value = 0.10 means p.value >= 0.10 \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "      lag          EG             p.value      \n",
       " Min.   :1   Min.   :-3.2911   Min.   :0.0322  \n",
       " 1st Qu.:1   1st Qu.:-2.4068   1st Qu.:0.0661  \n",
       " Median :1   Median :-1.5225   Median :0.1000  \n",
       " Mean   :1   Mean   :-1.2640   Mean   :0.0774  \n",
       " 3rd Qu.:1   3rd Qu.:-0.2504   3rd Qu.:0.1000  \n",
       " Max.   :1   Max.   : 1.0217   Max.   :0.1000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Response: relation$Y1 \n",
      "Input: relation$Y11 \n",
      "Number of inputs: 1 \n",
      "Model: y ~ X + 1 \n",
      "------------------------------- \n",
      "Engle-Granger Cointegration Test \n",
      "alternative: cointegrated \n",
      "\n",
      "Type 1: no trend \n",
      "    lag      EG p.value \n",
      " 1.0000 -3.2519  0.0344 \n",
      "----- \n",
      " Type 2: linear trend \n",
      "    lag      EG p.value \n",
      "   1.00   -1.48    0.10 \n",
      "----- \n",
      " Type 3: quadratic trend \n",
      "    lag      EG p.value \n",
      "  1.000   0.954   0.100 \n",
      "----------- \n",
      "Note: p.value = 0.01 means p.value <= 0.01 \n",
      "    : p.value = 0.10 means p.value >= 0.10 \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "      lag          EG             p.value       \n",
       " Min.   :1   Min.   :-3.2519   Min.   :0.03438  \n",
       " 1st Qu.:1   1st Qu.:-2.3684   1st Qu.:0.06719  \n",
       " Median :1   Median :-1.4849   Median :0.10000  \n",
       " Mean   :1   Mean   :-1.2609   Mean   :0.07813  \n",
       " 3rd Qu.:1   3rd Qu.:-0.2654   3rd Qu.:0.10000  \n",
       " Max.   :1   Max.   : 0.9540   Max.   :0.10000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Response: relation$Y2 \n",
      "Input: relation$Y3 \n",
      "Number of inputs: 1 \n",
      "Model: y ~ X + 1 \n",
      "------------------------------- \n",
      "Engle-Granger Cointegration Test \n",
      "alternative: cointegrated \n",
      "\n",
      "Type 1: no trend \n",
      "    lag      EG p.value \n",
      "   1.00   -4.65    0.01 \n",
      "----- \n",
      " Type 2: linear trend \n",
      "    lag      EG p.value \n",
      "   1.00   -1.04    0.10 \n",
      "----- \n",
      " Type 3: quadratic trend \n",
      "    lag      EG p.value \n",
      "   1.00    1.01    0.10 \n",
      "----------- \n",
      "Note: p.value = 0.01 means p.value <= 0.01 \n",
      "    : p.value = 0.10 means p.value >= 0.10 \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "      lag          EG              p.value     \n",
       " Min.   :1   Min.   :-4.64596   Min.   :0.010  \n",
       " 1st Qu.:1   1st Qu.:-2.84129   1st Qu.:0.055  \n",
       " Median :1   Median :-1.03662   Median :0.100  \n",
       " Mean   :1   Mean   :-1.55778   Mean   :0.070  \n",
       " 3rd Qu.:1   3rd Qu.:-0.01369   3rd Qu.:0.100  \n",
       " Max.   :1   Max.   : 1.00924   Max.   :0.100  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Response: relation$Y2 \n",
      "Input: relation$Y4 \n",
      "Number of inputs: 1 \n",
      "Model: y ~ X + 1 \n",
      "------------------------------- \n",
      "Engle-Granger Cointegration Test \n",
      "alternative: cointegrated \n",
      "\n",
      "Type 1: no trend \n",
      "    lag      EG p.value \n",
      "   1.00   -4.60    0.01 \n",
      "----- \n",
      " Type 2: linear trend \n",
      "    lag      EG p.value \n",
      "   1.00   -1.12    0.10 \n",
      "----- \n",
      " Type 3: quadratic trend \n",
      "    lag      EG p.value \n",
      "  1.000   0.952   0.100 \n",
      "----------- \n",
      "Note: p.value = 0.01 means p.value <= 0.01 \n",
      "    : p.value = 0.10 means p.value >= 0.10 \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "      lag          EG              p.value     \n",
       " Min.   :1   Min.   :-4.60105   Min.   :0.010  \n",
       " 1st Qu.:1   1st Qu.:-2.86161   1st Qu.:0.055  \n",
       " Median :1   Median :-1.12217   Median :0.100  \n",
       " Mean   :1   Mean   :-1.59034   Mean   :0.070  \n",
       " 3rd Qu.:1   3rd Qu.:-0.08498   3rd Qu.:0.100  \n",
       " Max.   :1   Max.   : 0.95220   Max.   :0.100  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Response: relation$Y2 \n",
      "Input: relation$Y6 \n",
      "Number of inputs: 1 \n",
      "Model: y ~ X + 1 \n",
      "------------------------------- \n",
      "Engle-Granger Cointegration Test \n",
      "alternative: cointegrated \n",
      "\n",
      "Type 1: no trend \n",
      "    lag      EG p.value \n",
      "   1.00   -4.72    0.01 \n",
      "----- \n",
      " Type 2: linear trend \n",
      "    lag      EG p.value \n",
      "  1.000  -0.775   0.100 \n",
      "----- \n",
      " Type 3: quadratic trend \n",
      "    lag      EG p.value \n",
      "    1.0     1.6     0.1 \n",
      "----------- \n",
      "Note: p.value = 0.01 means p.value <= 0.01 \n",
      "    : p.value = 0.10 means p.value >= 0.10 \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "      lag          EG             p.value     \n",
       " Min.   :1   Min.   :-4.7159   Min.   :0.010  \n",
       " 1st Qu.:1   1st Qu.:-2.7456   1st Qu.:0.055  \n",
       " Median :1   Median :-0.7754   Median :0.100  \n",
       " Mean   :1   Mean   :-1.2983   Mean   :0.070  \n",
       " 3rd Qu.:1   3rd Qu.: 0.4105   3rd Qu.:0.100  \n",
       " Max.   :1   Max.   : 1.5963   Max.   :0.100  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Response: relation$Y2 \n",
      "Input: relation$Y7 \n",
      "Number of inputs: 1 \n",
      "Model: y ~ X + 1 \n",
      "------------------------------- \n",
      "Engle-Granger Cointegration Test \n",
      "alternative: cointegrated \n",
      "\n",
      "Type 1: no trend \n",
      "    lag      EG p.value \n",
      "   1.00   -4.58    0.01 \n",
      "----- \n",
      " Type 2: linear trend \n",
      "    lag      EG p.value \n",
      "  1.000  -0.969   0.100 \n",
      "----- \n",
      " Type 3: quadratic trend \n",
      "    lag      EG p.value \n",
      "   1.00    1.02    0.10 \n",
      "----------- \n",
      "Note: p.value = 0.01 means p.value <= 0.01 \n",
      "    : p.value = 0.10 means p.value >= 0.10 \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "      lag          EG              p.value     \n",
       " Min.   :1   Min.   :-4.57641   Min.   :0.010  \n",
       " 1st Qu.:1   1st Qu.:-2.77293   1st Qu.:0.055  \n",
       " Median :1   Median :-0.96945   Median :0.100  \n",
       " Mean   :1   Mean   :-1.50750   Mean   :0.070  \n",
       " 3rd Qu.:1   3rd Qu.: 0.02696   3rd Qu.:0.100  \n",
       " Max.   :1   Max.   : 1.02337   Max.   :0.100  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Response: relation$Y2 \n",
      "Input: relation$Y8 \n",
      "Number of inputs: 1 \n",
      "Model: y ~ X + 1 \n",
      "------------------------------- \n",
      "Engle-Granger Cointegration Test \n",
      "alternative: cointegrated \n",
      "\n",
      "Type 1: no trend \n",
      "    lag      EG p.value \n",
      "   1.00   -4.48    0.01 \n",
      "----- \n",
      " Type 2: linear trend \n",
      "    lag      EG p.value \n",
      "  1.000  -0.965   0.100 \n",
      "----- \n",
      " Type 3: quadratic trend \n",
      "    lag      EG p.value \n",
      "  1.000   0.699   0.100 \n",
      "----------- \n",
      "Note: p.value = 0.01 means p.value <= 0.01 \n",
      "    : p.value = 0.10 means p.value >= 0.10 \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "      lag          EG             p.value     \n",
       " Min.   :1   Min.   :-4.4775   Min.   :0.010  \n",
       " 1st Qu.:1   1st Qu.:-2.7210   1st Qu.:0.055  \n",
       " Median :1   Median :-0.9645   Median :0.100  \n",
       " Mean   :1   Mean   :-1.5812   Mean   :0.070  \n",
       " 3rd Qu.:1   3rd Qu.:-0.1330   3rd Qu.:0.100  \n",
       " Max.   :1   Max.   : 0.6985   Max.   :0.100  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Response: relation$Y2 \n",
      "Input: relation$Y9 \n",
      "Number of inputs: 1 \n",
      "Model: y ~ X + 1 \n",
      "------------------------------- \n",
      "Engle-Granger Cointegration Test \n",
      "alternative: cointegrated \n",
      "\n",
      "Type 1: no trend \n",
      "    lag      EG p.value \n",
      "   1.00   -4.51    0.01 \n",
      "----- \n",
      " Type 2: linear trend \n",
      "    lag      EG p.value \n",
      "  1.000  -0.999   0.100 \n",
      "----- \n",
      " Type 3: quadratic trend \n",
      "    lag      EG p.value \n",
      "  1.000   0.848   0.100 \n",
      "----------- \n",
      "Note: p.value = 0.01 means p.value <= 0.01 \n",
      "    : p.value = 0.10 means p.value >= 0.10 \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "      lag          EG              p.value     \n",
       " Min.   :1   Min.   :-4.50577   Min.   :0.010  \n",
       " 1st Qu.:1   1st Qu.:-2.75255   1st Qu.:0.055  \n",
       " Median :1   Median :-0.99932   Median :0.100  \n",
       " Mean   :1   Mean   :-1.55238   Mean   :0.070  \n",
       " 3rd Qu.:1   3rd Qu.:-0.07568   3rd Qu.:0.100  \n",
       " Max.   :1   Max.   : 0.84796   Max.   :0.100  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Response: relation$Y2 \n",
      "Input: relation$Y10 \n",
      "Number of inputs: 1 \n",
      "Model: y ~ X + 1 \n",
      "------------------------------- \n",
      "Engle-Granger Cointegration Test \n",
      "alternative: cointegrated \n",
      "\n",
      "Type 1: no trend \n",
      "    lag      EG p.value \n",
      "   1.00   -3.88    0.01 \n",
      "----- \n",
      " Type 2: linear trend \n",
      "    lag      EG p.value \n",
      "   1.00   -1.13    0.10 \n",
      "----- \n",
      " Type 3: quadratic trend \n",
      "    lag      EG p.value \n",
      "    1.0     1.2     0.1 \n",
      "----------- \n",
      "Note: p.value = 0.01 means p.value <= 0.01 \n",
      "    : p.value = 0.10 means p.value >= 0.10 \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "      lag          EG              p.value     \n",
       " Min.   :1   Min.   :-3.88207   Min.   :0.010  \n",
       " 1st Qu.:1   1st Qu.:-2.50522   1st Qu.:0.055  \n",
       " Median :1   Median :-1.12836   Median :0.100  \n",
       " Mean   :1   Mean   :-1.27100   Mean   :0.070  \n",
       " 3rd Qu.:1   3rd Qu.: 0.03454   3rd Qu.:0.100  \n",
       " Max.   :1   Max.   : 1.19745   Max.   :0.100  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Response: relation$Y2 \n",
      "Input: relation$Y11 \n",
      "Number of inputs: 1 \n",
      "Model: y ~ X + 1 \n",
      "------------------------------- \n",
      "Engle-Granger Cointegration Test \n",
      "alternative: cointegrated \n",
      "\n",
      "Type 1: no trend \n",
      "    lag      EG p.value \n",
      "   1.00   -4.34    0.01 \n",
      "----- \n",
      " Type 2: linear trend \n",
      "    lag      EG p.value \n",
      "  1.000  -0.977   0.100 \n",
      "----- \n",
      " Type 3: quadratic trend \n",
      "    lag      EG p.value \n",
      "  1.000   0.881   0.100 \n",
      "----------- \n",
      "Note: p.value = 0.01 means p.value <= 0.01 \n",
      "    : p.value = 0.10 means p.value >= 0.10 \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "      lag          EG              p.value     \n",
       " Min.   :1   Min.   :-4.34443   Min.   :0.010  \n",
       " 1st Qu.:1   1st Qu.:-2.66085   1st Qu.:0.055  \n",
       " Median :1   Median :-0.97727   Median :0.100  \n",
       " Mean   :1   Mean   :-1.48012   Mean   :0.070  \n",
       " 3rd Qu.:1   3rd Qu.:-0.04797   3rd Qu.:0.100  \n",
       " Max.   :1   Max.   : 0.88133   Max.   :0.100  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Response: relation$Y3 \n",
      "Input: relation$Y4 \n",
      "Number of inputs: 1 \n",
      "Model: y ~ X + 1 \n",
      "------------------------------- \n",
      "Engle-Granger Cointegration Test \n",
      "alternative: cointegrated \n",
      "\n",
      "Type 1: no trend \n",
      "    lag      EG p.value \n",
      "   1.00   -8.77    0.01 \n",
      "----- \n",
      " Type 2: linear trend \n",
      "    lag      EG p.value \n",
      " 1.0000  0.0447  0.1000 \n",
      "----- \n",
      " Type 3: quadratic trend \n",
      "    lag      EG p.value \n",
      "  1.000  -0.264   0.100 \n",
      "----------- \n",
      "Note: p.value = 0.01 means p.value <= 0.01 \n",
      "    : p.value = 0.10 means p.value >= 0.10 \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "      lag          EG              p.value     \n",
       " Min.   :1   Min.   :-8.77391   Min.   :0.010  \n",
       " 1st Qu.:1   1st Qu.:-4.51906   1st Qu.:0.055  \n",
       " Median :1   Median :-0.26422   Median :0.100  \n",
       " Mean   :1   Mean   :-2.99782   Mean   :0.070  \n",
       " 3rd Qu.:1   3rd Qu.:-0.10977   3rd Qu.:0.100  \n",
       " Max.   :1   Max.   : 0.04467   Max.   :0.100  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Response: relation$Y3 \n",
      "Input: relation$Y5 \n",
      "Number of inputs: 1 \n",
      "Model: y ~ X + 1 \n",
      "------------------------------- \n",
      "Engle-Granger Cointegration Test \n",
      "alternative: cointegrated \n",
      "\n",
      "Type 1: no trend \n",
      "    lag      EG p.value \n",
      "   1.00   -9.95    0.01 \n",
      "----- \n",
      " Type 2: linear trend \n",
      "     lag       EG  p.value \n",
      " 1.00000 -0.00191  0.10000 \n",
      "----- \n",
      " Type 3: quadratic trend \n",
      "    lag      EG p.value \n",
      "  1.000  -0.107   0.100 \n",
      "----------- \n",
      "Note: p.value = 0.01 means p.value <= 0.01 \n",
      "    : p.value = 0.10 means p.value >= 0.10 \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "      lag          EG               p.value     \n",
       " Min.   :1   Min.   :-9.946011   Min.   :0.010  \n",
       " 1st Qu.:1   1st Qu.:-5.026447   1st Qu.:0.055  \n",
       " Median :1   Median :-0.106884   Median :0.100  \n",
       " Mean   :1   Mean   :-3.351600   Mean   :0.070  \n",
       " 3rd Qu.:1   3rd Qu.:-0.054395   3rd Qu.:0.100  \n",
       " Max.   :1   Max.   :-0.001905   Max.   :0.100  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Response: relation$Y3 \n",
      "Input: relation$Y6 \n",
      "Number of inputs: 1 \n",
      "Model: y ~ X + 1 \n",
      "------------------------------- \n",
      "Engle-Granger Cointegration Test \n",
      "alternative: cointegrated \n",
      "\n",
      "Type 1: no trend \n",
      "    lag      EG p.value \n",
      "   1.00   -9.93    0.01 \n",
      "----- \n",
      " Type 2: linear trend \n",
      "    lag      EG p.value \n",
      "  1.000   0.215   0.100 \n",
      "----- \n",
      " Type 3: quadratic trend \n",
      "    lag      EG p.value \n",
      "  1.000  -0.161   0.100 \n",
      "----------- \n",
      "Note: p.value = 0.01 means p.value <= 0.01 \n",
      "    : p.value = 0.10 means p.value >= 0.10 \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "      lag          EG              p.value     \n",
       " Min.   :1   Min.   :-9.92875   Min.   :0.010  \n",
       " 1st Qu.:1   1st Qu.:-5.04467   1st Qu.:0.055  \n",
       " Median :1   Median :-0.16060   Median :0.100  \n",
       " Mean   :1   Mean   :-3.29161   Mean   :0.070  \n",
       " 3rd Qu.:1   3rd Qu.: 0.02695   3rd Qu.:0.100  \n",
       " Max.   :1   Max.   : 0.21451   Max.   :0.100  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Response: relation$Y3 \n",
      "Input: relation$Y7 \n",
      "Number of inputs: 1 \n",
      "Model: y ~ X + 1 \n",
      "------------------------------- \n",
      "Engle-Granger Cointegration Test \n",
      "alternative: cointegrated \n",
      "\n",
      "Type 1: no trend \n",
      "    lag      EG p.value \n",
      "   1.00   -9.99    0.01 \n",
      "----- \n",
      " Type 2: linear trend \n",
      "    lag      EG p.value \n",
      "  1.000   0.103   0.100 \n",
      "----- \n",
      " Type 3: quadratic trend \n",
      "    lag      EG p.value \n",
      "  1.000  -0.177   0.100 \n",
      "----------- \n",
      "Note: p.value = 0.01 means p.value <= 0.01 \n",
      "    : p.value = 0.10 means p.value >= 0.10 \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "      lag          EG              p.value     \n",
       " Min.   :1   Min.   :-9.98558   Min.   :0.010  \n",
       " 1st Qu.:1   1st Qu.:-5.08111   1st Qu.:0.055  \n",
       " Median :1   Median :-0.17664   Median :0.100  \n",
       " Mean   :1   Mean   :-3.35304   Mean   :0.070  \n",
       " 3rd Qu.:1   3rd Qu.:-0.03677   3rd Qu.:0.100  \n",
       " Max.   :1   Max.   : 0.10311   Max.   :0.100  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Response: relation$Y3 \n",
      "Input: relation$Y8 \n",
      "Number of inputs: 1 \n",
      "Model: y ~ X + 1 \n",
      "------------------------------- \n",
      "Engle-Granger Cointegration Test \n",
      "alternative: cointegrated \n",
      "\n",
      "Type 1: no trend \n",
      "    lag      EG p.value \n",
      "   1.00   -9.94    0.01 \n",
      "----- \n",
      " Type 2: linear trend \n",
      "    lag      EG p.value \n",
      "  1.000   0.195   0.100 \n",
      "----- \n",
      " Type 3: quadratic trend \n",
      "    lag      EG p.value \n",
      "  1.000  -0.127   0.100 \n",
      "----------- \n",
      "Note: p.value = 0.01 means p.value <= 0.01 \n",
      "    : p.value = 0.10 means p.value >= 0.10 \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "      lag          EG              p.value     \n",
       " Min.   :1   Min.   :-9.94315   Min.   :0.010  \n",
       " 1st Qu.:1   1st Qu.:-5.03483   1st Qu.:0.055  \n",
       " Median :1   Median :-0.12651   Median :0.100  \n",
       " Mean   :1   Mean   :-3.29150   Mean   :0.070  \n",
       " 3rd Qu.:1   3rd Qu.: 0.03433   3rd Qu.:0.100  \n",
       " Max.   :1   Max.   : 0.19517   Max.   :0.100  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Response: relation$Y3 \n",
      "Input: relation$Y9 \n",
      "Number of inputs: 1 \n",
      "Model: y ~ X + 1 \n",
      "------------------------------- \n",
      "Engle-Granger Cointegration Test \n",
      "alternative: cointegrated \n",
      "\n",
      "Type 1: no trend \n",
      "    lag      EG p.value \n",
      "   1.00   -9.23    0.01 \n",
      "----- \n",
      " Type 2: linear trend \n",
      "    lag      EG p.value \n",
      "  1.000   0.286   0.100 \n",
      "----- \n",
      " Type 3: quadratic trend \n",
      "    lag      EG p.value \n",
      "  1.000  -0.621   0.100 \n",
      "----------- \n",
      "Note: p.value = 0.01 means p.value <= 0.01 \n",
      "    : p.value = 0.10 means p.value >= 0.10 \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "      lag          EG             p.value     \n",
       " Min.   :1   Min.   :-9.2342   Min.   :0.010  \n",
       " 1st Qu.:1   1st Qu.:-4.9275   1st Qu.:0.055  \n",
       " Median :1   Median :-0.6207   Median :0.100  \n",
       " Mean   :1   Mean   :-3.1898   Mean   :0.070  \n",
       " 3rd Qu.:1   3rd Qu.:-0.1676   3rd Qu.:0.100  \n",
       " Max.   :1   Max.   : 0.2855   Max.   :0.100  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Response: relation$Y3 \n",
      "Input: relation$Y10 \n",
      "Number of inputs: 1 \n",
      "Model: y ~ X + 1 \n",
      "------------------------------- \n",
      "Engle-Granger Cointegration Test \n",
      "alternative: cointegrated \n",
      "\n",
      "Type 1: no trend \n",
      "    lag      EG p.value \n",
      "   1.00   -9.86    0.01 \n",
      "----- \n",
      " Type 2: linear trend \n",
      "    lag      EG p.value \n",
      "  1.000   0.188   0.100 \n",
      "----- \n",
      " Type 3: quadratic trend \n",
      "    lag      EG p.value \n",
      "  1.000  -0.143   0.100 \n",
      "----------- \n",
      "Note: p.value = 0.01 means p.value <= 0.01 \n",
      "    : p.value = 0.10 means p.value >= 0.10 \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "      lag          EG              p.value     \n",
       " Min.   :1   Min.   :-9.86300   Min.   :0.010  \n",
       " 1st Qu.:1   1st Qu.:-5.00297   1st Qu.:0.055  \n",
       " Median :1   Median :-0.14294   Median :0.100  \n",
       " Mean   :1   Mean   :-3.27278   Mean   :0.070  \n",
       " 3rd Qu.:1   3rd Qu.: 0.02233   3rd Qu.:0.100  \n",
       " Max.   :1   Max.   : 0.18760   Max.   :0.100  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Response: relation$Y3 \n",
      "Input: relation$Y11 \n",
      "Number of inputs: 1 \n",
      "Model: y ~ X + 1 \n",
      "------------------------------- \n",
      "Engle-Granger Cointegration Test \n",
      "alternative: cointegrated \n",
      "\n",
      "Type 1: no trend \n",
      "    lag      EG p.value \n",
      "   1.00   -9.91    0.01 \n",
      "----- \n",
      " Type 2: linear trend \n",
      "    lag      EG p.value \n",
      "   1.00    0.19    0.10 \n",
      "----- \n",
      " Type 3: quadratic trend \n",
      "    lag      EG p.value \n",
      "  1.000  -0.265   0.100 \n",
      "----------- \n",
      "Note: p.value = 0.01 means p.value <= 0.01 \n",
      "    : p.value = 0.10 means p.value >= 0.10 \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "      lag          EG              p.value     \n",
       " Min.   :1   Min.   :-9.91151   Min.   :0.010  \n",
       " 1st Qu.:1   1st Qu.:-5.08808   1st Qu.:0.055  \n",
       " Median :1   Median :-0.26466   Median :0.100  \n",
       " Mean   :1   Mean   :-3.32879   Mean   :0.070  \n",
       " 3rd Qu.:1   3rd Qu.:-0.03744   3rd Qu.:0.100  \n",
       " Max.   :1   Max.   : 0.18979   Max.   :0.100  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Response: relation$Y4 \n",
      "Input: relation$Y5 \n",
      "Number of inputs: 1 \n",
      "Model: y ~ X + 1 \n",
      "------------------------------- \n",
      "Engle-Granger Cointegration Test \n",
      "alternative: cointegrated \n",
      "\n",
      "Type 1: no trend \n",
      "    lag      EG p.value \n",
      "   1.00   -5.67    0.01 \n",
      "----- \n",
      " Type 2: linear trend \n",
      "    lag      EG p.value \n",
      "  1.000   0.411   0.100 \n",
      "----- \n",
      " Type 3: quadratic trend \n",
      "    lag      EG p.value \n",
      "  1.000   0.332   0.100 \n",
      "----------- \n",
      "Note: p.value = 0.01 means p.value <= 0.01 \n",
      "    : p.value = 0.10 means p.value >= 0.10 \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "      lag          EG             p.value     \n",
       " Min.   :1   Min.   :-5.6652   Min.   :0.010  \n",
       " 1st Qu.:1   1st Qu.:-2.6668   1st Qu.:0.055  \n",
       " Median :1   Median : 0.3317   Median :0.100  \n",
       " Mean   :1   Mean   :-1.6409   Mean   :0.070  \n",
       " 3rd Qu.:1   3rd Qu.: 0.3713   3rd Qu.:0.100  \n",
       " Max.   :1   Max.   : 0.4109   Max.   :0.100  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Response: relation$Y4 \n",
      "Input: relation$Y6 \n",
      "Number of inputs: 1 \n",
      "Model: y ~ X + 1 \n",
      "------------------------------- \n",
      "Engle-Granger Cointegration Test \n",
      "alternative: cointegrated \n",
      "\n",
      "Type 1: no trend \n",
      "    lag      EG p.value \n",
      "   1.00   -5.56    0.01 \n",
      "----- \n",
      " Type 2: linear trend \n",
      "    lag      EG p.value \n",
      "  1.000   0.401   0.100 \n",
      "----- \n",
      " Type 3: quadratic trend \n",
      "    lag      EG p.value \n",
      "   1.00    0.47    0.10 \n",
      "----------- \n",
      "Note: p.value = 0.01 means p.value <= 0.01 \n",
      "    : p.value = 0.10 means p.value >= 0.10 \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "      lag          EG             p.value     \n",
       " Min.   :1   Min.   :-5.5603   Min.   :0.010  \n",
       " 1st Qu.:1   1st Qu.:-2.5794   1st Qu.:0.055  \n",
       " Median :1   Median : 0.4015   Median :0.100  \n",
       " Mean   :1   Mean   :-1.5630   Mean   :0.070  \n",
       " 3rd Qu.:1   3rd Qu.: 0.4357   3rd Qu.:0.100  \n",
       " Max.   :1   Max.   : 0.4699   Max.   :0.100  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Response: relation$Y4 \n",
      "Input: relation$Y7 \n",
      "Number of inputs: 1 \n",
      "Model: y ~ X + 1 \n",
      "------------------------------- \n",
      "Engle-Granger Cointegration Test \n",
      "alternative: cointegrated \n",
      "\n",
      "Type 1: no trend \n",
      "    lag      EG p.value \n",
      "   1.00   -6.05    0.01 \n",
      "----- \n",
      " Type 2: linear trend \n",
      "    lag      EG p.value \n",
      "  1.000   0.196   0.100 \n",
      "----- \n",
      " Type 3: quadratic trend \n",
      "    lag      EG p.value \n",
      "  1.000   0.331   0.100 \n",
      "----------- \n",
      "Note: p.value = 0.01 means p.value <= 0.01 \n",
      "    : p.value = 0.10 means p.value >= 0.10 \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "      lag          EG             p.value     \n",
       " Min.   :1   Min.   :-6.0521   Min.   :0.010  \n",
       " 1st Qu.:1   1st Qu.:-2.9281   1st Qu.:0.055  \n",
       " Median :1   Median : 0.1959   Median :0.100  \n",
       " Mean   :1   Mean   :-1.8417   Mean   :0.070  \n",
       " 3rd Qu.:1   3rd Qu.: 0.2635   3rd Qu.:0.100  \n",
       " Max.   :1   Max.   : 0.3311   Max.   :0.100  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Response: relation$Y4 \n",
      "Input: relation$Y8 \n",
      "Number of inputs: 1 \n",
      "Model: y ~ X + 1 \n",
      "------------------------------- \n",
      "Engle-Granger Cointegration Test \n",
      "alternative: cointegrated \n",
      "\n",
      "Type 1: no trend \n",
      "    lag      EG p.value \n",
      "   1.00   -5.44    0.01 \n",
      "----- \n",
      " Type 2: linear trend \n",
      "    lag      EG p.value \n",
      "  1.000   0.353   0.100 \n",
      "----- \n",
      " Type 3: quadratic trend \n",
      "    lag      EG p.value \n",
      "  1.000   0.109   0.100 \n",
      "----------- \n",
      "Note: p.value = 0.01 means p.value <= 0.01 \n",
      "    : p.value = 0.10 means p.value >= 0.10 \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "      lag          EG             p.value     \n",
       " Min.   :1   Min.   :-5.4426   Min.   :0.010  \n",
       " 1st Qu.:1   1st Qu.:-2.6669   1st Qu.:0.055  \n",
       " Median :1   Median : 0.1088   Median :0.100  \n",
       " Mean   :1   Mean   :-1.6601   Mean   :0.070  \n",
       " 3rd Qu.:1   3rd Qu.: 0.2311   3rd Qu.:0.100  \n",
       " Max.   :1   Max.   : 0.3535   Max.   :0.100  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Response: relation$Y4 \n",
      "Input: relation$Y9 \n",
      "Number of inputs: 1 \n",
      "Model: y ~ X + 1 \n",
      "------------------------------- \n",
      "Engle-Granger Cointegration Test \n",
      "alternative: cointegrated \n",
      "\n",
      "Type 1: no trend \n",
      "    lag      EG p.value \n",
      "   1.00   -5.55    0.01 \n",
      "----- \n",
      " Type 2: linear trend \n",
      "    lag      EG p.value \n",
      "  1.000   0.339   0.100 \n",
      "----- \n",
      " Type 3: quadratic trend \n",
      "    lag      EG p.value \n",
      "  1.000   0.208   0.100 \n",
      "----------- \n",
      "Note: p.value = 0.01 means p.value <= 0.01 \n",
      "    : p.value = 0.10 means p.value >= 0.10 \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "      lag          EG             p.value     \n",
       " Min.   :1   Min.   :-5.5530   Min.   :0.010  \n",
       " 1st Qu.:1   1st Qu.:-2.6727   1st Qu.:0.055  \n",
       " Median :1   Median : 0.2077   Median :0.100  \n",
       " Mean   :1   Mean   :-1.6687   Mean   :0.070  \n",
       " 3rd Qu.:1   3rd Qu.: 0.2734   3rd Qu.:0.100  \n",
       " Max.   :1   Max.   : 0.3391   Max.   :0.100  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Response: relation$Y4 \n",
      "Input: relation$Y10 \n",
      "Number of inputs: 1 \n",
      "Model: y ~ X + 1 \n",
      "------------------------------- \n",
      "Engle-Granger Cointegration Test \n",
      "alternative: cointegrated \n",
      "\n",
      "Type 1: no trend \n",
      "    lag      EG p.value \n",
      "   1.00   -5.67    0.01 \n",
      "----- \n",
      " Type 2: linear trend \n",
      "    lag      EG p.value \n",
      "  1.000   0.226   0.100 \n",
      "----- \n",
      " Type 3: quadratic trend \n",
      "    lag      EG p.value \n",
      "  1.000   0.581   0.100 \n",
      "----------- \n",
      "Note: p.value = 0.01 means p.value <= 0.01 \n",
      "    : p.value = 0.10 means p.value >= 0.10 \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "      lag          EG             p.value     \n",
       " Min.   :1   Min.   :-5.6703   Min.   :0.010  \n",
       " 1st Qu.:1   1st Qu.:-2.7221   1st Qu.:0.055  \n",
       " Median :1   Median : 0.2262   Median :0.100  \n",
       " Mean   :1   Mean   :-1.6211   Mean   :0.070  \n",
       " 3rd Qu.:1   3rd Qu.: 0.4035   3rd Qu.:0.100  \n",
       " Max.   :1   Max.   : 0.5808   Max.   :0.100  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Response: relation$Y4 \n",
      "Input: relation$Y11 \n",
      "Number of inputs: 1 \n",
      "Model: y ~ X + 1 \n",
      "------------------------------- \n",
      "Engle-Granger Cointegration Test \n",
      "alternative: cointegrated \n",
      "\n",
      "Type 1: no trend \n",
      "    lag      EG p.value \n",
      "   1.00   -5.17    0.01 \n",
      "----- \n",
      " Type 2: linear trend \n",
      "    lag      EG p.value \n",
      "  1.000   0.329   0.100 \n",
      "----- \n",
      " Type 3: quadratic trend \n",
      "    lag      EG p.value \n",
      "  1.000   0.268   0.100 \n",
      "----------- \n",
      "Note: p.value = 0.01 means p.value <= 0.01 \n",
      "    : p.value = 0.10 means p.value >= 0.10 \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "      lag          EG             p.value     \n",
       " Min.   :1   Min.   :-5.1739   Min.   :0.010  \n",
       " 1st Qu.:1   1st Qu.:-2.4531   1st Qu.:0.055  \n",
       " Median :1   Median : 0.2678   Median :0.100  \n",
       " Mean   :1   Mean   :-1.5256   Mean   :0.070  \n",
       " 3rd Qu.:1   3rd Qu.: 0.2985   3rd Qu.:0.100  \n",
       " Max.   :1   Max.   : 0.3293   Max.   :0.100  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Response: relation$Y5 \n",
      "Input: relation$Y6 \n",
      "Number of inputs: 1 \n",
      "Model: y ~ X + 1 \n",
      "------------------------------- \n",
      "Engle-Granger Cointegration Test \n",
      "alternative: cointegrated \n",
      "\n",
      "Type 1: no trend \n",
      "    lag      EG p.value \n",
      "   1.00   -3.78    0.01 \n",
      "----- \n",
      " Type 2: linear trend \n",
      "    lag      EG p.value \n",
      "  1.000  -0.705   0.100 \n",
      "----- \n",
      " Type 3: quadratic trend \n",
      "    lag      EG p.value \n",
      "  1.000   0.388   0.100 \n",
      "----------- \n",
      "Note: p.value = 0.01 means p.value <= 0.01 \n",
      "    : p.value = 0.10 means p.value >= 0.10 \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "      lag          EG             p.value     \n",
       " Min.   :1   Min.   :-3.7776   Min.   :0.010  \n",
       " 1st Qu.:1   1st Qu.:-2.2413   1st Qu.:0.055  \n",
       " Median :1   Median :-0.7051   Median :0.100  \n",
       " Mean   :1   Mean   :-1.3650   Mean   :0.070  \n",
       " 3rd Qu.:1   3rd Qu.:-0.1587   3rd Qu.:0.100  \n",
       " Max.   :1   Max.   : 0.3877   Max.   :0.100  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Response: relation$Y5 \n",
      "Input: relation$Y7 \n",
      "Number of inputs: 1 \n",
      "Model: y ~ X + 1 \n",
      "------------------------------- \n",
      "Engle-Granger Cointegration Test \n",
      "alternative: cointegrated \n",
      "\n",
      "Type 1: no trend \n",
      "    lag      EG p.value \n",
      "   1.00   -3.72    0.01 \n",
      "----- \n",
      " Type 2: linear trend \n",
      "    lag      EG p.value \n",
      "  1.000  -0.806   0.100 \n",
      "----- \n",
      " Type 3: quadratic trend \n",
      "    lag      EG p.value \n",
      "  1.000   0.205   0.100 \n",
      "----------- \n",
      "Note: p.value = 0.01 means p.value <= 0.01 \n",
      "    : p.value = 0.10 means p.value >= 0.10 \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "      lag          EG             p.value     \n",
       " Min.   :1   Min.   :-3.7180   Min.   :0.010  \n",
       " 1st Qu.:1   1st Qu.:-2.2621   1st Qu.:0.055  \n",
       " Median :1   Median :-0.8062   Median :0.100  \n",
       " Mean   :1   Mean   :-1.4397   Mean   :0.070  \n",
       " 3rd Qu.:1   3rd Qu.:-0.3006   3rd Qu.:0.100  \n",
       " Max.   :1   Max.   : 0.2051   Max.   :0.100  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Response: relation$Y5 \n",
      "Input: relation$Y8 \n",
      "Number of inputs: 1 \n",
      "Model: y ~ X + 1 \n",
      "------------------------------- \n",
      "Engle-Granger Cointegration Test \n",
      "alternative: cointegrated \n",
      "\n",
      "Type 1: no trend \n",
      "    lag      EG p.value \n",
      "  1.000  -3.528   0.019 \n",
      "----- \n",
      " Type 2: linear trend \n",
      "    lag      EG p.value \n",
      "   1.00   -0.71    0.10 \n",
      "----- \n",
      " Type 3: quadratic trend \n",
      "    lag      EG p.value \n",
      "  1.000  -0.133   0.100 \n",
      "----------- \n",
      "Note: p.value = 0.01 means p.value <= 0.01 \n",
      "    : p.value = 0.10 means p.value >= 0.10 \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "      lag          EG             p.value       \n",
       " Min.   :1   Min.   :-3.5283   Min.   :0.01896  \n",
       " 1st Qu.:1   1st Qu.:-2.1192   1st Qu.:0.05948  \n",
       " Median :1   Median :-0.7101   Median :0.10000  \n",
       " Mean   :1   Mean   :-1.4571   Mean   :0.07299  \n",
       " 3rd Qu.:1   3rd Qu.:-0.4215   3rd Qu.:0.10000  \n",
       " Max.   :1   Max.   :-0.1329   Max.   :0.10000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Response: relation$Y5 \n",
      "Input: relation$Y9 \n",
      "Number of inputs: 1 \n",
      "Model: y ~ X + 1 \n",
      "------------------------------- \n",
      "Engle-Granger Cointegration Test \n",
      "alternative: cointegrated \n",
      "\n",
      "Type 1: no trend \n",
      "    lag      EG p.value \n",
      "   1.00   -3.73    0.01 \n",
      "----- \n",
      " Type 2: linear trend \n",
      "    lag      EG p.value \n",
      "   1.00   -0.82    0.10 \n",
      "----- \n",
      " Type 3: quadratic trend \n",
      "    lag      EG p.value \n",
      "  1.000   0.232   0.100 \n",
      "----------- \n",
      "Note: p.value = 0.01 means p.value <= 0.01 \n",
      "    : p.value = 0.10 means p.value >= 0.10 \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "      lag          EG             p.value     \n",
       " Min.   :1   Min.   :-3.7271   Min.   :0.010  \n",
       " 1st Qu.:1   1st Qu.:-2.2737   1st Qu.:0.055  \n",
       " Median :1   Median :-0.8204   Median :0.100  \n",
       " Mean   :1   Mean   :-1.4386   Mean   :0.070  \n",
       " 3rd Qu.:1   3rd Qu.:-0.2944   3rd Qu.:0.100  \n",
       " Max.   :1   Max.   : 0.2316   Max.   :0.100  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Response: relation$Y5 \n",
      "Input: relation$Y10 \n",
      "Number of inputs: 1 \n",
      "Model: y ~ X + 1 \n",
      "------------------------------- \n",
      "Engle-Granger Cointegration Test \n",
      "alternative: cointegrated \n",
      "\n",
      "Type 1: no trend \n",
      "    lag      EG p.value \n",
      "   1.00   -3.85    0.01 \n",
      "----- \n",
      " Type 2: linear trend \n",
      "    lag      EG p.value \n",
      "  1.000  -0.811   0.100 \n",
      "----- \n",
      " Type 3: quadratic trend \n",
      "    lag      EG p.value \n",
      "  1.000   0.179   0.100 \n",
      "----------- \n",
      "Note: p.value = 0.01 means p.value <= 0.01 \n",
      "    : p.value = 0.10 means p.value >= 0.10 \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "      lag          EG             p.value     \n",
       " Min.   :1   Min.   :-3.8541   Min.   :0.010  \n",
       " 1st Qu.:1   1st Qu.:-2.3325   1st Qu.:0.055  \n",
       " Median :1   Median :-0.8109   Median :0.100  \n",
       " Mean   :1   Mean   :-1.4953   Mean   :0.070  \n",
       " 3rd Qu.:1   3rd Qu.:-0.3158   3rd Qu.:0.100  \n",
       " Max.   :1   Max.   : 0.1792   Max.   :0.100  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Response: relation$Y5 \n",
      "Input: relation$Y11 \n",
      "Number of inputs: 1 \n",
      "Model: y ~ X + 1 \n",
      "------------------------------- \n",
      "Engle-Granger Cointegration Test \n",
      "alternative: cointegrated \n",
      "\n",
      "Type 1: no trend \n",
      "    lag      EG p.value \n",
      "   1.00   -3.76    0.01 \n",
      "----- \n",
      " Type 2: linear trend \n",
      "    lag      EG p.value \n",
      "  1.000  -0.799   0.100 \n",
      "----- \n",
      " Type 3: quadratic trend \n",
      "    lag      EG p.value \n",
      "  1.000   0.176   0.100 \n",
      "----------- \n",
      "Note: p.value = 0.01 means p.value <= 0.01 \n",
      "    : p.value = 0.10 means p.value >= 0.10 \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "      lag          EG             p.value     \n",
       " Min.   :1   Min.   :-3.7604   Min.   :0.010  \n",
       " 1st Qu.:1   1st Qu.:-2.2798   1st Qu.:0.055  \n",
       " Median :1   Median :-0.7992   Median :0.100  \n",
       " Mean   :1   Mean   :-1.4611   Mean   :0.070  \n",
       " 3rd Qu.:1   3rd Qu.:-0.3114   3rd Qu.:0.100  \n",
       " Max.   :1   Max.   : 0.1765   Max.   :0.100  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Response: relation$Y6 \n",
      "Input: relation$Y7 \n",
      "Number of inputs: 1 \n",
      "Model: y ~ X + 1 \n",
      "------------------------------- \n",
      "Engle-Granger Cointegration Test \n",
      "alternative: cointegrated \n",
      "\n",
      "Type 1: no trend \n",
      "    lag      EG p.value \n",
      " 1.0000 -3.0426  0.0461 \n",
      "----- \n",
      " Type 2: linear trend \n",
      "    lag      EG p.value \n",
      "   1.00    0.44    0.10 \n",
      "----- \n",
      " Type 3: quadratic trend \n",
      "    lag      EG p.value \n",
      "  1.000   0.716   0.100 \n",
      "----------- \n",
      "Note: p.value = 0.01 means p.value <= 0.01 \n",
      "    : p.value = 0.10 means p.value >= 0.10 \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "      lag          EG             p.value       \n",
       " Min.   :1   Min.   :-3.0426   Min.   :0.04606  \n",
       " 1st Qu.:1   1st Qu.:-1.3014   1st Qu.:0.07303  \n",
       " Median :1   Median : 0.4398   Median :0.10000  \n",
       " Mean   :1   Mean   :-0.6288   Mean   :0.08202  \n",
       " 3rd Qu.:1   3rd Qu.: 0.5781   3rd Qu.:0.10000  \n",
       " Max.   :1   Max.   : 0.7163   Max.   :0.10000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Response: relation$Y6 \n",
      "Input: relation$Y8 \n",
      "Number of inputs: 1 \n",
      "Model: y ~ X + 1 \n",
      "------------------------------- \n",
      "Engle-Granger Cointegration Test \n",
      "alternative: cointegrated \n",
      "\n",
      "Type 1: no trend \n",
      "    lag      EG p.value \n",
      " 1.0000 -3.4531  0.0232 \n",
      "----- \n",
      " Type 2: linear trend \n",
      "    lag      EG p.value \n",
      "  1.000   0.894   0.100 \n",
      "----- \n",
      " Type 3: quadratic trend \n",
      "    lag      EG p.value \n",
      "  1.000   0.669   0.100 \n",
      "----------- \n",
      "Note: p.value = 0.01 means p.value <= 0.01 \n",
      "    : p.value = 0.10 means p.value >= 0.10 \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "      lag          EG             p.value       \n",
       " Min.   :1   Min.   :-3.4531   Min.   :0.02316  \n",
       " 1st Qu.:1   1st Qu.:-1.3922   1st Qu.:0.06158  \n",
       " Median :1   Median : 0.6686   Median :0.10000  \n",
       " Mean   :1   Mean   :-0.6303   Mean   :0.07439  \n",
       " 3rd Qu.:1   3rd Qu.: 0.7812   3rd Qu.:0.10000  \n",
       " Max.   :1   Max.   : 0.8937   Max.   :0.10000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Response: relation$Y6 \n",
      "Input: relation$Y9 \n",
      "Number of inputs: 1 \n",
      "Model: y ~ X + 1 \n",
      "------------------------------- \n",
      "Engle-Granger Cointegration Test \n",
      "alternative: cointegrated \n",
      "\n",
      "Type 1: no trend \n",
      "    lag      EG p.value \n",
      "   1.00   -2.23    0.10 \n",
      "----- \n",
      " Type 2: linear trend \n",
      "    lag      EG p.value \n",
      "  1.000   0.267   0.100 \n",
      "----- \n",
      " Type 3: quadratic trend \n",
      "    lag      EG p.value \n",
      "   1.00    1.11    0.10 \n",
      "----------- \n",
      "Note: p.value = 0.01 means p.value <= 0.01 \n",
      "    : p.value = 0.10 means p.value >= 0.10 \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "      lag          EG             p.value   \n",
       " Min.   :1   Min.   :-2.2285   Min.   :0.1  \n",
       " 1st Qu.:1   1st Qu.:-0.9806   1st Qu.:0.1  \n",
       " Median :1   Median : 0.2673   Median :0.1  \n",
       " Mean   :1   Mean   :-0.2838   Mean   :0.1  \n",
       " 3rd Qu.:1   3rd Qu.: 0.6885   3rd Qu.:0.1  \n",
       " Max.   :1   Max.   : 1.1097   Max.   :0.1  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Response: relation$Y6 \n",
      "Input: relation$Y10 \n",
      "Number of inputs: 1 \n",
      "Model: y ~ X + 1 \n",
      "------------------------------- \n",
      "Engle-Granger Cointegration Test \n",
      "alternative: cointegrated \n",
      "\n",
      "Type 1: no trend \n",
      "    lag      EG p.value \n",
      "   1.00   -2.16    0.10 \n",
      "----- \n",
      " Type 2: linear trend \n",
      "    lag      EG p.value \n",
      "  1.000   0.263   0.100 \n",
      "----- \n",
      " Type 3: quadratic trend \n",
      "    lag      EG p.value \n",
      "   1.00    1.12    0.10 \n",
      "----------- \n",
      "Note: p.value = 0.01 means p.value <= 0.01 \n",
      "    : p.value = 0.10 means p.value >= 0.10 \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "      lag          EG             p.value   \n",
       " Min.   :1   Min.   :-2.1580   Min.   :0.1  \n",
       " 1st Qu.:1   1st Qu.:-0.9477   1st Qu.:0.1  \n",
       " Median :1   Median : 0.2626   Median :0.1  \n",
       " Mean   :1   Mean   :-0.2597   Mean   :0.1  \n",
       " 3rd Qu.:1   3rd Qu.: 0.6894   3rd Qu.:0.1  \n",
       " Max.   :1   Max.   : 1.1163   Max.   :0.1  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Response: relation$Y6 \n",
      "Input: relation$Y11 \n",
      "Number of inputs: 1 \n",
      "Model: y ~ X + 1 \n",
      "------------------------------- \n",
      "Engle-Granger Cointegration Test \n",
      "alternative: cointegrated \n",
      "\n",
      "Type 1: no trend \n",
      "    lag      EG p.value \n",
      "   1.00   -1.91    0.10 \n",
      "----- \n",
      " Type 2: linear trend \n",
      "    lag      EG p.value \n",
      " 1.0000  0.0381  0.1000 \n",
      "----- \n",
      " Type 3: quadratic trend \n",
      "    lag      EG p.value \n",
      "   1.00    1.34    0.10 \n",
      "----------- \n",
      "Note: p.value = 0.01 means p.value <= 0.01 \n",
      "    : p.value = 0.10 means p.value >= 0.10 \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "      lag          EG             p.value   \n",
       " Min.   :1   Min.   :-1.9064   Min.   :0.1  \n",
       " 1st Qu.:1   1st Qu.:-0.9342   1st Qu.:0.1  \n",
       " Median :1   Median : 0.0381   Median :0.1  \n",
       " Mean   :1   Mean   :-0.1773   Mean   :0.1  \n",
       " 3rd Qu.:1   3rd Qu.: 0.6873   3rd Qu.:0.1  \n",
       " Max.   :1   Max.   : 1.3366   Max.   :0.1  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Response: relation$Y7 \n",
      "Input: relation$Y8 \n",
      "Number of inputs: 1 \n",
      "Model: y ~ X + 1 \n",
      "------------------------------- \n",
      "Engle-Granger Cointegration Test \n",
      "alternative: cointegrated \n",
      "\n",
      "Type 1: no trend \n",
      "    lag      EG p.value \n",
      " 1.0000 -2.6821  0.0918 \n",
      "----- \n",
      " Type 2: linear trend \n",
      "    lag      EG p.value \n",
      "  1.000  -0.462   0.100 \n",
      "----- \n",
      " Type 3: quadratic trend \n",
      "    lag      EG p.value \n",
      "  1.000  -0.382   0.100 \n",
      "----------- \n",
      "Note: p.value = 0.01 means p.value <= 0.01 \n",
      "    : p.value = 0.10 means p.value >= 0.10 \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "      lag          EG             p.value       \n",
       " Min.   :1   Min.   :-2.6821   Min.   :0.09181  \n",
       " 1st Qu.:1   1st Qu.:-1.5723   1st Qu.:0.09590  \n",
       " Median :1   Median :-0.4625   Median :0.10000  \n",
       " Mean   :1   Mean   :-1.1754   Mean   :0.09727  \n",
       " 3rd Qu.:1   3rd Qu.:-0.4221   3rd Qu.:0.10000  \n",
       " Max.   :1   Max.   :-0.3818   Max.   :0.10000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Response: relation$Y7 \n",
      "Input: relation$Y9 \n",
      "Number of inputs: 1 \n",
      "Model: y ~ X + 1 \n",
      "------------------------------- \n",
      "Engle-Granger Cointegration Test \n",
      "alternative: cointegrated \n",
      "\n",
      "Type 1: no trend \n",
      "    lag      EG p.value \n",
      "   1.00   -1.87    0.10 \n",
      "----- \n",
      " Type 2: linear trend \n",
      "    lag      EG p.value \n",
      "  1.000   0.197   0.100 \n",
      "----- \n",
      " Type 3: quadratic trend \n",
      "    lag      EG p.value \n",
      "   1.00   -1.27    0.10 \n",
      "----------- \n",
      "Note: p.value = 0.01 means p.value <= 0.01 \n",
      "    : p.value = 0.10 means p.value >= 0.10 \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "      lag          EG             p.value   \n",
       " Min.   :1   Min.   :-1.8717   Min.   :0.1  \n",
       " 1st Qu.:1   1st Qu.:-1.5702   1st Qu.:0.1  \n",
       " Median :1   Median :-1.2686   Median :0.1  \n",
       " Mean   :1   Mean   :-0.9809   Mean   :0.1  \n",
       " 3rd Qu.:1   3rd Qu.:-0.5355   3rd Qu.:0.1  \n",
       " Max.   :1   Max.   : 0.1975   Max.   :0.1  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Response: relation$Y7 \n",
      "Input: relation$Y10 \n",
      "Number of inputs: 1 \n",
      "Model: y ~ X + 1 \n",
      "------------------------------- \n",
      "Engle-Granger Cointegration Test \n",
      "alternative: cointegrated \n",
      "\n",
      "Type 1: no trend \n",
      "    lag      EG p.value \n",
      "    1.0    -2.1     0.1 \n",
      "----- \n",
      " Type 2: linear trend \n",
      "    lag      EG p.value \n",
      " 1.0000  0.0373  0.1000 \n",
      "----- \n",
      " Type 3: quadratic trend \n",
      "    lag      EG p.value \n",
      "   1.00   -1.03    0.10 \n",
      "----------- \n",
      "Note: p.value = 0.01 means p.value <= 0.01 \n",
      "    : p.value = 0.10 means p.value >= 0.10 \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "      lag          EG              p.value   \n",
       " Min.   :1   Min.   :-2.09866   Min.   :0.1  \n",
       " 1st Qu.:1   1st Qu.:-1.56302   1st Qu.:0.1  \n",
       " Median :1   Median :-1.02738   Median :0.1  \n",
       " Mean   :1   Mean   :-1.02959   Mean   :0.1  \n",
       " 3rd Qu.:1   3rd Qu.:-0.49505   3rd Qu.:0.1  \n",
       " Max.   :1   Max.   : 0.03729   Max.   :0.1  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Response: relation$Y7 \n",
      "Input: relation$Y11 \n",
      "Number of inputs: 1 \n",
      "Model: y ~ X + 1 \n",
      "------------------------------- \n",
      "Engle-Granger Cointegration Test \n",
      "alternative: cointegrated \n",
      "\n",
      "Type 1: no trend \n",
      "    lag      EG p.value \n",
      "   1.00   -2.11    0.10 \n",
      "----- \n",
      " Type 2: linear trend \n",
      "    lag      EG p.value \n",
      " 1.0000  0.0536  0.1000 \n",
      "----- \n",
      " Type 3: quadratic trend \n",
      "    lag      EG p.value \n",
      "   1.00   -1.05    0.10 \n",
      "----------- \n",
      "Note: p.value = 0.01 means p.value <= 0.01 \n",
      "    : p.value = 0.10 means p.value >= 0.10 \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "      lag          EG              p.value   \n",
       " Min.   :1   Min.   :-2.11205   Min.   :0.1  \n",
       " 1st Qu.:1   1st Qu.:-1.58009   1st Qu.:0.1  \n",
       " Median :1   Median :-1.04812   Median :0.1  \n",
       " Mean   :1   Mean   :-1.03553   Mean   :0.1  \n",
       " 3rd Qu.:1   3rd Qu.:-0.49727   3rd Qu.:0.1  \n",
       " Max.   :1   Max.   : 0.05358   Max.   :0.1  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Response: relation$Y8 \n",
      "Input: relation$Y9 \n",
      "Number of inputs: 1 \n",
      "Model: y ~ X + 1 \n",
      "------------------------------- \n",
      "Engle-Granger Cointegration Test \n",
      "alternative: cointegrated \n",
      "\n",
      "Type 1: no trend \n",
      "    lag      EG p.value \n",
      " 1.0000 -2.9494  0.0533 \n",
      "----- \n",
      " Type 2: linear trend \n",
      "    lag      EG p.value \n",
      "  1.000  -0.374   0.100 \n",
      "----- \n",
      " Type 3: quadratic trend \n",
      "    lag      EG p.value \n",
      "   1.00    1.06    0.10 \n",
      "----------- \n",
      "Note: p.value = 0.01 means p.value <= 0.01 \n",
      "    : p.value = 0.10 means p.value >= 0.10 \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "      lag          EG             p.value       \n",
       " Min.   :1   Min.   :-2.9494   Min.   :0.05325  \n",
       " 1st Qu.:1   1st Qu.:-1.6616   1st Qu.:0.07663  \n",
       " Median :1   Median :-0.3737   Median :0.10000  \n",
       " Mean   :1   Mean   :-0.7554   Mean   :0.08442  \n",
       " 3rd Qu.:1   3rd Qu.: 0.3415   3rd Qu.:0.10000  \n",
       " Max.   :1   Max.   : 1.0568   Max.   :0.10000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Response: relation$Y8 \n",
      "Input: relation$Y10 \n",
      "Number of inputs: 1 \n",
      "Model: y ~ X + 1 \n",
      "------------------------------- \n",
      "Engle-Granger Cointegration Test \n",
      "alternative: cointegrated \n",
      "\n",
      "Type 1: no trend \n",
      "    lag      EG p.value \n",
      " 1.0000 -3.1138  0.0421 \n",
      "----- \n",
      " Type 2: linear trend \n",
      "    lag      EG p.value \n",
      "  1.000  -0.344   0.100 \n",
      "----- \n",
      " Type 3: quadratic trend \n",
      "    lag      EG p.value \n",
      "   1.00    1.06    0.10 \n",
      "----------- \n",
      "Note: p.value = 0.01 means p.value <= 0.01 \n",
      "    : p.value = 0.10 means p.value >= 0.10 \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "      lag          EG             p.value       \n",
       " Min.   :1   Min.   :-3.1138   Min.   :0.04208  \n",
       " 1st Qu.:1   1st Qu.:-1.7289   1st Qu.:0.07104  \n",
       " Median :1   Median :-0.3439   Median :0.10000  \n",
       " Mean   :1   Mean   :-0.7989   Mean   :0.08069  \n",
       " 3rd Qu.:1   3rd Qu.: 0.3586   3rd Qu.:0.10000  \n",
       " Max.   :1   Max.   : 1.0611   Max.   :0.10000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Response: relation$Y8 \n",
      "Input: relation$Y11 \n",
      "Number of inputs: 1 \n",
      "Model: y ~ X + 1 \n",
      "------------------------------- \n",
      "Engle-Granger Cointegration Test \n",
      "alternative: cointegrated \n",
      "\n",
      "Type 1: no trend \n",
      "    lag      EG p.value \n",
      " 1.0000 -2.9041  0.0598 \n",
      "----- \n",
      " Type 2: linear trend \n",
      "    lag      EG p.value \n",
      "  1.000  -0.365   0.100 \n",
      "----- \n",
      " Type 3: quadratic trend \n",
      "    lag      EG p.value \n",
      "   1.00    1.04    0.10 \n",
      "----------- \n",
      "Note: p.value = 0.01 means p.value <= 0.01 \n",
      "    : p.value = 0.10 means p.value >= 0.10 \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "      lag          EG             p.value       \n",
       " Min.   :1   Min.   :-2.9041   Min.   :0.05979  \n",
       " 1st Qu.:1   1st Qu.:-1.6344   1st Qu.:0.07989  \n",
       " Median :1   Median :-0.3647   Median :0.10000  \n",
       " Mean   :1   Mean   :-0.7445   Mean   :0.08660  \n",
       " 3rd Qu.:1   3rd Qu.: 0.3353   3rd Qu.:0.10000  \n",
       " Max.   :1   Max.   : 1.0353   Max.   :0.10000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Response: relation$Y9 \n",
      "Input: relation$Y10 \n",
      "Number of inputs: 1 \n",
      "Model: y ~ X + 1 \n",
      "------------------------------- \n",
      "Engle-Granger Cointegration Test \n",
      "alternative: cointegrated \n",
      "\n",
      "Type 1: no trend \n",
      "    lag      EG p.value \n",
      "   1.00   -4.99    0.01 \n",
      "----- \n",
      " Type 2: linear trend \n",
      "    lag      EG p.value \n",
      "  1.000   0.213   0.100 \n",
      "----- \n",
      " Type 3: quadratic trend \n",
      "    lag      EG p.value \n",
      "   1.00   -1.07    0.10 \n",
      "----------- \n",
      "Note: p.value = 0.01 means p.value <= 0.01 \n",
      "    : p.value = 0.10 means p.value >= 0.10 \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "      lag          EG             p.value     \n",
       " Min.   :1   Min.   :-4.9897   Min.   :0.010  \n",
       " 1st Qu.:1   1st Qu.:-3.0290   1st Qu.:0.055  \n",
       " Median :1   Median :-1.0682   Median :0.100  \n",
       " Mean   :1   Mean   :-1.9485   Mean   :0.070  \n",
       " 3rd Qu.:1   3rd Qu.:-0.4278   3rd Qu.:0.100  \n",
       " Max.   :1   Max.   : 0.2125   Max.   :0.100  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Response: relation$Y9 \n",
      "Input: relation$Y11 \n",
      "Number of inputs: 1 \n",
      "Model: y ~ X + 1 \n",
      "------------------------------- \n",
      "Engle-Granger Cointegration Test \n",
      "alternative: cointegrated \n",
      "\n",
      "Type 1: no trend \n",
      "    lag      EG p.value \n",
      "   1.00   -4.77    0.01 \n",
      "----- \n",
      " Type 2: linear trend \n",
      "    lag      EG p.value \n",
      "  1.000   0.219   0.100 \n",
      "----- \n",
      " Type 3: quadratic trend \n",
      "    lag      EG p.value \n",
      "   1.00   -1.05    0.10 \n",
      "----------- \n",
      "Note: p.value = 0.01 means p.value <= 0.01 \n",
      "    : p.value = 0.10 means p.value >= 0.10 \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "      lag          EG             p.value     \n",
       " Min.   :1   Min.   :-4.7739   Min.   :0.010  \n",
       " 1st Qu.:1   1st Qu.:-2.9112   1st Qu.:0.055  \n",
       " Median :1   Median :-1.0485   Median :0.100  \n",
       " Mean   :1   Mean   :-1.8679   Mean   :0.070  \n",
       " 3rd Qu.:1   3rd Qu.:-0.4148   3rd Qu.:0.100  \n",
       " Max.   :1   Max.   : 0.2188   Max.   :0.100  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Response: relation$Y10 \n",
      "Input: relation$Y11 \n",
      "Number of inputs: 1 \n",
      "Model: y ~ X + 1 \n",
      "------------------------------- \n",
      "Engle-Granger Cointegration Test \n",
      "alternative: cointegrated \n",
      "\n",
      "Type 1: no trend \n",
      "    lag      EG p.value \n",
      "   1.00   -4.42    0.01 \n",
      "----- \n",
      " Type 2: linear trend \n",
      "    lag      EG p.value \n",
      "  1.000   0.324   0.100 \n",
      "----- \n",
      " Type 3: quadratic trend \n",
      "    lag      EG p.value \n",
      "  1.000  -0.601   0.100 \n",
      "----------- \n",
      "Note: p.value = 0.01 means p.value <= 0.01 \n",
      "    : p.value = 0.10 means p.value >= 0.10 \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "      lag          EG             p.value     \n",
       " Min.   :1   Min.   :-4.4199   Min.   :0.010  \n",
       " 1st Qu.:1   1st Qu.:-2.5104   1st Qu.:0.055  \n",
       " Median :1   Median :-0.6009   Median :0.100  \n",
       " Mean   :1   Mean   :-1.5657   Mean   :0.070  \n",
       " 3rd Qu.:1   3rd Qu.:-0.1386   3rd Qu.:0.100  \n",
       " Max.   :1   Max.   : 0.3237   Max.   :0.100  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Y1～11\n",
    "coint.test(relation$Y1, relation$Y2,  nlag = 1) %>% summary\n",
    "coint.test(relation$Y1, relation$Y3,  nlag = 1) %>% summary\n",
    "coint.test(relation$Y1, relation$Y4,  nlag = 1) %>% summary\n",
    "coint.test(relation$Y1, relation$Y5,  nlag = 1) %>% summary\n",
    "coint.test(relation$Y1, relation$Y6,  nlag = 1) %>% summary\n",
    "coint.test(relation$Y1, relation$Y7,  nlag = 1) %>% summary\n",
    "coint.test(relation$Y1, relation$Y8,  nlag = 1) %>% summary\n",
    "coint.test(relation$Y1, relation$Y9,  nlag = 1) %>% summary\n",
    "coint.test(relation$Y1, relation$Y10, nlag = 1) %>% summary\n",
    "coint.test(relation$Y1, relation$Y11, nlag = 1) %>% summary\n",
    "#Y2～11\n",
    "coint.test(relation$Y2, relation$Y3,  nlag = 1) %>% summary\n",
    "coint.test(relation$Y2, relation$Y4,  nlag = 1) %>% summary\n",
    "coint.test(relation$Y2, relation$Y6,  nlag = 1) %>% summary\n",
    "coint.test(relation$Y2, relation$Y7,  nlag = 1) %>% summary\n",
    "coint.test(relation$Y2, relation$Y8,  nlag = 1) %>% summary\n",
    "coint.test(relation$Y2, relation$Y9,  nlag = 1) %>% summary\n",
    "coint.test(relation$Y2, relation$Y10, nlag = 1) %>% summary\n",
    "coint.test(relation$Y2, relation$Y11, nlag = 1) %>% summary\n",
    "#Y3～11\n",
    "coint.test(relation$Y3, relation$Y4,  nlag = 1) %>% summary\n",
    "coint.test(relation$Y3, relation$Y5,  nlag = 1) %>% summary\n",
    "coint.test(relation$Y3, relation$Y6,  nlag = 1) %>% summary\n",
    "coint.test(relation$Y3, relation$Y7,  nlag = 1) %>% summary\n",
    "coint.test(relation$Y3, relation$Y8,  nlag = 1) %>% summary\n",
    "coint.test(relation$Y3, relation$Y9,  nlag = 1) %>% summary\n",
    "coint.test(relation$Y3, relation$Y10, nlag = 1) %>% summary\n",
    "coint.test(relation$Y3, relation$Y11, nlag = 1) %>% summary\n",
    "#Y4～11\n",
    "coint.test(relation$Y4, relation$Y5,  nlag = 1) %>% summary\n",
    "coint.test(relation$Y4, relation$Y6,  nlag = 1) %>% summary\n",
    "coint.test(relation$Y4, relation$Y7,  nlag = 1) %>% summary\n",
    "coint.test(relation$Y4, relation$Y8,  nlag = 1) %>% summary\n",
    "coint.test(relation$Y4, relation$Y9,  nlag = 1) %>% summary\n",
    "coint.test(relation$Y4, relation$Y10, nlag = 1) %>% summary\n",
    "coint.test(relation$Y4, relation$Y11, nlag = 1) %>% summary\n",
    "#Y5～11\n",
    "coint.test(relation$Y5, relation$Y6,  nlag = 1) %>% summary\n",
    "coint.test(relation$Y5, relation$Y7,  nlag = 1) %>% summary\n",
    "coint.test(relation$Y5, relation$Y8,  nlag = 1) %>% summary\n",
    "coint.test(relation$Y5, relation$Y9,  nlag = 1) %>% summary\n",
    "coint.test(relation$Y5, relation$Y10, nlag = 1) %>% summary\n",
    "coint.test(relation$Y5, relation$Y11, nlag = 1) %>% summary\n",
    "#Y6～11\n",
    "coint.test(relation$Y6, relation$Y7,  nlag = 1) %>% summary\n",
    "coint.test(relation$Y6, relation$Y8,  nlag = 1) %>% summary\n",
    "coint.test(relation$Y6, relation$Y9,  nlag = 1) %>% summary\n",
    "coint.test(relation$Y6, relation$Y10, nlag = 1) %>% summary\n",
    "coint.test(relation$Y6, relation$Y11, nlag = 1) %>% summary\n",
    "#Y7～11\n",
    "coint.test(relation$Y7, relation$Y8,  nlag = 1) %>% summary\n",
    "coint.test(relation$Y7, relation$Y9,  nlag = 1) %>% summary\n",
    "coint.test(relation$Y7, relation$Y10, nlag = 1) %>% summary\n",
    "coint.test(relation$Y7, relation$Y11, nlag = 1) %>% summary\n",
    "#Y8～11\n",
    "coint.test(relation$Y8, relation$Y9,  nlag = 1) %>% summary\n",
    "coint.test(relation$Y8, relation$Y10, nlag = 1) %>% summary\n",
    "coint.test(relation$Y8, relation$Y11, nlag = 1) %>% summary\n",
    "#Y9～11\n",
    "coint.test(relation$Y9, relation$Y10, nlag = 1) %>% summary\n",
    "coint.test(relation$Y9, relation$Y11, nlag = 1) %>% summary\n",
    "#Y10～11\n",
    "coint.test(relation$Y10, relation$Y11, nlag = 1) %>% summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 偏グレンジャー因果性検定と非直交化インパルス応答関数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[1]]\n",
       "[[1]][[1]]\n",
       "[[1]][[1]]$Granger\n",
       "\n",
       "\tGranger causality H0: y1 do not Granger-cause y2\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 2.6256, df1 = 2, df2 = 40, p-value = 0.08484\n",
       "\n",
       "\n",
       "[[1]][[1]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y1 and y2\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 7.1644, df = 1, p-value = 0.007437\n",
       "\n",
       "\n",
       "\n",
       "[[1]][[2]]\n",
       "[[1]][[2]]$Granger\n",
       "\n",
       "\tGranger causality H0: y2 do not Granger-cause y1\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 0.16326, df1 = 2, df2 = 40, p-value = 0.8499\n",
       "\n",
       "\n",
       "[[1]][[2]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y2 and y1\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 7.1644, df = 1, p-value = 0.007437\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "[[2]]\n",
       "[[2]][[1]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y1\n",
       "                 y2\n",
       " [1,]  0.0217465331\n",
       " [2,]  0.0013100522\n",
       " [3,]  0.0045246853\n",
       " [4,] -0.0067526973\n",
       " [5,] -0.0068970660\n",
       " [6,]  0.0092147037\n",
       " [7,]  0.0039736520\n",
       " [8,] -0.0092305452\n",
       " [9,] -0.0006671813\n",
       "[10,]  0.0078670658\n",
       "[11,] -0.0018027779\n",
       "\n",
       "\n",
       "[[2]][[2]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y2\n",
       "                 y1\n",
       " [1,]  0.0000000000\n",
       " [2,] -0.0008288689\n",
       " [3,]  0.0027036435\n",
       " [4,] -0.0001887940\n",
       " [5,] -0.0027562308\n",
       " [6,]  0.0010641781\n",
       " [7,]  0.0020658498\n",
       " [8,] -0.0015265323\n",
       " [9,] -0.0012500648\n",
       "[10,]  0.0016329930\n",
       "[11,]  0.0005278046\n",
       "\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[[1]]\n",
       "[[1]][[1]]\n",
       "[[1]][[1]]$Granger\n",
       "\n",
       "\tGranger causality H0: y1 do not Granger-cause y2\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 0.74651, df1 = 2, df2 = 40, p-value = 0.4805\n",
       "\n",
       "\n",
       "[[1]][[1]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y1 and y2\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 0.032199, df = 1, p-value = 0.8576\n",
       "\n",
       "\n",
       "\n",
       "[[1]][[2]]\n",
       "[[1]][[2]]$Granger\n",
       "\n",
       "\tGranger causality H0: y2 do not Granger-cause y1\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 1.5637, df1 = 2, df2 = 40, p-value = 0.2219\n",
       "\n",
       "\n",
       "[[1]][[2]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y2 and y1\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 0.032199, df = 1, p-value = 0.8576\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "[[2]]\n",
       "[[2]][[1]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y1\n",
       "                 y2\n",
       " [1,] -9.040977e-03\n",
       " [2,] -3.784293e-02\n",
       " [3,]  1.911729e-03\n",
       " [4,]  3.691991e-02\n",
       " [5,] -2.074645e-02\n",
       " [6,] -6.640440e-03\n",
       " [7,]  1.093324e-02\n",
       " [8,] -4.518784e-03\n",
       " [9,]  1.391421e-03\n",
       "[10,]  7.750355e-05\n",
       "[11,] -2.040931e-03\n",
       "\n",
       "\n",
       "[[2]][[2]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y2\n",
       "                 y1\n",
       " [1,]  0.0000000000\n",
       " [2,]  0.0046221443\n",
       " [3,]  0.0020075561\n",
       " [4,] -0.0062476015\n",
       " [5,]  0.0023742709\n",
       " [6,]  0.0017110317\n",
       " [7,] -0.0018783764\n",
       " [8,]  0.0006879277\n",
       " [9,] -0.0001110905\n",
       "[10,] -0.0001815815\n",
       "[11,]  0.0004287487\n",
       "\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[[1]]\n",
       "[[1]][[1]]\n",
       "[[1]][[1]]$Granger\n",
       "\n",
       "\tGranger causality H0: y1 do not Granger-cause y2\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 1.4966, df1 = 2, df2 = 40, p-value = 0.2362\n",
       "\n",
       "\n",
       "[[1]][[1]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y1 and y2\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 1.5877, df = 1, p-value = 0.2077\n",
       "\n",
       "\n",
       "\n",
       "[[1]][[2]]\n",
       "[[1]][[2]]$Granger\n",
       "\n",
       "\tGranger causality H0: y2 do not Granger-cause y1\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 0.77623, df1 = 2, df2 = 40, p-value = 0.4669\n",
       "\n",
       "\n",
       "[[1]][[2]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y2 and y1\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 1.5877, df = 1, p-value = 0.2077\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "[[2]]\n",
       "[[2]][[1]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y1\n",
       "                y2\n",
       " [1,]  0.049694639\n",
       " [2,]  0.060199122\n",
       " [3,] -0.025674351\n",
       " [4,] -0.032148549\n",
       " [5,]  0.014708010\n",
       " [6,]  0.013561987\n",
       " [7,] -0.007651633\n",
       " [8,] -0.005308786\n",
       " [9,]  0.003744787\n",
       "[10,]  0.001994851\n",
       "[11,] -0.001769536\n",
       "\n",
       "\n",
       "[[2]][[2]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y2\n",
       "                 y1\n",
       " [1,]  0.0000000000\n",
       " [2,] -0.0052748671\n",
       " [3,]  0.0005680558\n",
       " [4,]  0.0028751466\n",
       " [5,] -0.0005841470\n",
       " [6,] -0.0012564721\n",
       " [7,]  0.0003729502\n",
       " [8,]  0.0005159424\n",
       " [9,] -0.0002039720\n",
       "[10,] -0.0002060324\n",
       "[11,]  0.0001040500\n",
       "\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[[1]]\n",
       "[[1]][[1]]\n",
       "[[1]][[1]]$Granger\n",
       "\n",
       "\tGranger causality H0: y1 do not Granger-cause y2\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 0.77671, df1 = 2, df2 = 40, p-value = 0.4667\n",
       "\n",
       "\n",
       "[[1]][[1]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y1 and y2\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 0.024614, df = 1, p-value = 0.8753\n",
       "\n",
       "\n",
       "\n",
       "[[1]][[2]]\n",
       "[[1]][[2]]$Granger\n",
       "\n",
       "\tGranger causality H0: y2 do not Granger-cause y1\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 3.2556, df1 = 2, df2 = 40, p-value = 0.04898\n",
       "\n",
       "\n",
       "[[1]][[2]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y2 and y1\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 0.024614, df = 1, p-value = 0.8753\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "[[2]]\n",
       "[[2]][[1]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y1\n",
       "                 y2\n",
       " [1,]  2.448691e-03\n",
       " [2,] -3.870686e-03\n",
       " [3,] -1.900878e-02\n",
       " [4,] -2.815092e-04\n",
       " [5,]  1.401182e-02\n",
       " [6,]  2.688601e-03\n",
       " [7,] -5.233008e-03\n",
       " [8,] -2.122562e-03\n",
       " [9,] -3.220022e-05\n",
       "[10,]  5.840635e-04\n",
       "[11,]  1.470619e-03\n",
       "\n",
       "\n",
       "[[2]][[2]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y2\n",
       "                 y1\n",
       " [1,]  0.000000e+00\n",
       " [2,]  2.725505e-05\n",
       " [3,]  9.260411e-03\n",
       " [4,]  1.678013e-03\n",
       " [5,] -6.995328e-03\n",
       " [6,] -2.033865e-03\n",
       " [7,]  2.646696e-03\n",
       " [8,]  1.179241e-03\n",
       " [9,] -3.232899e-05\n",
       "[10,] -1.982043e-04\n",
       "[11,] -6.809711e-04\n",
       "\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[[1]]\n",
       "[[1]][[1]]\n",
       "[[1]][[1]]$Granger\n",
       "\n",
       "\tGranger causality H0: y1 do not Granger-cause y2\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 3.1308, df1 = 2, df2 = 40, p-value = 0.05455\n",
       "\n",
       "\n",
       "[[1]][[1]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y1 and y2\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 6.8459, df = 1, p-value = 0.008885\n",
       "\n",
       "\n",
       "\n",
       "[[1]][[2]]\n",
       "[[1]][[2]]$Granger\n",
       "\n",
       "\tGranger causality H0: y2 do not Granger-cause y1\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 1.1466, df1 = 2, df2 = 40, p-value = 0.3279\n",
       "\n",
       "\n",
       "[[1]][[2]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y2 and y1\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 6.8459, df = 1, p-value = 0.008885\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "[[2]]\n",
       "[[2]][[1]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y1\n",
       "                y2\n",
       " [1,] -0.019739860\n",
       " [2,] -0.027218278\n",
       " [3,] -0.017310674\n",
       " [4,] -0.006807237\n",
       " [5,] -0.006123428\n",
       " [6,] -0.006468563\n",
       " [7,] -0.004636910\n",
       " [8,] -0.002690773\n",
       " [9,] -0.001999658\n",
       "[10,] -0.001728456\n",
       "[11,] -0.001294256\n",
       "\n",
       "\n",
       "[[2]][[2]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y2\n",
       "                 y1\n",
       " [1,]  0.000000e+00\n",
       " [2,]  5.464107e-03\n",
       " [3,] -2.739384e-04\n",
       " [4,] -4.650811e-04\n",
       " [5,]  3.218252e-04\n",
       " [6,]  7.261115e-04\n",
       " [7,]  1.940497e-04\n",
       " [8,]  7.267797e-06\n",
       " [9,]  8.988276e-05\n",
       "[10,]  1.311073e-04\n",
       "[11,]  6.753764e-05\n",
       "\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[[1]]\n",
       "[[1]][[1]]\n",
       "[[1]][[1]]$Granger\n",
       "\n",
       "\tGranger causality H0: y1 do not Granger-cause y2\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 4.4742, df1 = 2, df2 = 40, p-value = 0.01764\n",
       "\n",
       "\n",
       "[[1]][[1]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y1 and y2\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 0.060658, df = 1, p-value = 0.8055\n",
       "\n",
       "\n",
       "\n",
       "[[1]][[2]]\n",
       "[[1]][[2]]$Granger\n",
       "\n",
       "\tGranger causality H0: y2 do not Granger-cause y1\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 1.1487, df1 = 2, df2 = 40, p-value = 0.3273\n",
       "\n",
       "\n",
       "[[1]][[2]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y2 and y1\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 0.060658, df = 1, p-value = 0.8055\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "[[2]]\n",
       "[[2]][[1]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y1\n",
       "                 y2\n",
       " [1,] -0.0004454833\n",
       " [2,]  0.0050419635\n",
       " [3,]  0.0056006794\n",
       " [4,]  0.0016769547\n",
       " [5,]  0.0003230013\n",
       " [6,]  0.0010103273\n",
       " [7,]  0.0011592038\n",
       " [8,]  0.0005616860\n",
       " [9,]  0.0002051397\n",
       "[10,]  0.0002312751\n",
       "[11,]  0.0002510731\n",
       "\n",
       "\n",
       "[[2]][[2]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y2\n",
       "                 y1\n",
       " [1,]  0.000000e+00\n",
       " [2,] -7.305935e-03\n",
       " [3,] -1.038472e-03\n",
       " [4,]  9.208687e-04\n",
       " [5,] -2.822756e-04\n",
       " [6,] -1.124965e-03\n",
       " [7,] -4.693709e-04\n",
       " [8,]  4.089431e-05\n",
       " [9,] -5.818749e-05\n",
       "[10,] -2.001449e-04\n",
       "[11,] -1.239648e-04\n",
       "\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[[1]]\n",
       "[[1]][[1]]\n",
       "[[1]][[1]]$Granger\n",
       "\n",
       "\tGranger causality H0: y1 do not Granger-cause y2\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 16.364, df1 = 2, df2 = 40, p-value = 6.414e-06\n",
       "\n",
       "\n",
       "[[1]][[1]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y1 and y2\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 3.034, df = 1, p-value = 0.08154\n",
       "\n",
       "\n",
       "\n",
       "[[1]][[2]]\n",
       "[[1]][[2]]$Granger\n",
       "\n",
       "\tGranger causality H0: y2 do not Granger-cause y1\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 3.1729, df1 = 2, df2 = 40, p-value = 0.0526\n",
       "\n",
       "\n",
       "[[1]][[2]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y2 and y1\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 3.034, df = 1, p-value = 0.08154\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "[[2]]\n",
       "[[2]][[1]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y1\n",
       "                 y2\n",
       " [1,]  0.0207611184\n",
       " [2,] -0.0267753604\n",
       " [3,] -0.0633002072\n",
       " [4,] -0.0161613794\n",
       " [5,]  0.0065730193\n",
       " [6,] -0.0009533390\n",
       " [7,]  0.0118288739\n",
       " [8,]  0.0152005372\n",
       " [9,]  0.0003001932\n",
       "[10,] -0.0031545299\n",
       "[11,] -0.0006869671\n",
       "\n",
       "\n",
       "[[2]][[2]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y2\n",
       "                 y1\n",
       " [1,]  0.000000e+00\n",
       " [2,] -4.804039e-03\n",
       " [3,]  4.843799e-03\n",
       " [4,]  5.306380e-03\n",
       " [5,] -1.050245e-03\n",
       " [6,] -1.645999e-05\n",
       " [7,]  7.474339e-04\n",
       " [8,] -1.715662e-03\n",
       " [9,] -1.040745e-03\n",
       "[10,]  5.359354e-04\n",
       "[11,] -5.186834e-06\n",
       "\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[[1]]\n",
       "[[1]][[1]]\n",
       "[[1]][[1]]$Granger\n",
       "\n",
       "\tGranger causality H0: y1 do not Granger-cause y2\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 0.058468, df1 = 2, df2 = 40, p-value = 0.9433\n",
       "\n",
       "\n",
       "[[1]][[1]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y1 and y2\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 3.7509, df = 1, p-value = 0.05278\n",
       "\n",
       "\n",
       "\n",
       "[[1]][[2]]\n",
       "[[1]][[2]]$Granger\n",
       "\n",
       "\tGranger causality H0: y2 do not Granger-cause y1\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 3.7068, df1 = 2, df2 = 40, p-value = 0.03335\n",
       "\n",
       "\n",
       "[[1]][[2]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y2 and y1\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 3.7509, df = 1, p-value = 0.05278\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "[[2]]\n",
       "[[2]][[1]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y1\n",
       "                 y2\n",
       " [1,]  0.1109406587\n",
       " [2,] -0.0117437910\n",
       " [3,] -0.0305054841\n",
       " [4,]  0.0055376933\n",
       " [5,]  0.0099605845\n",
       " [6,] -0.0023658523\n",
       " [7,] -0.0032784751\n",
       " [8,]  0.0009640955\n",
       " [9,]  0.0010715724\n",
       "[10,] -0.0003802085\n",
       "[11,] -0.0003471541\n",
       "\n",
       "\n",
       "[[2]][[2]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y2\n",
       "                 y1\n",
       " [1,]  0.000000e+00\n",
       " [2,]  8.501453e-03\n",
       " [3,] -6.475907e-03\n",
       " [4,] -2.720434e-03\n",
       " [5,]  2.497963e-03\n",
       " [6,]  8.088718e-04\n",
       " [7,] -8.947906e-04\n",
       " [8,] -2.328369e-04\n",
       " [9,]  3.164147e-04\n",
       "[10,]  6.420408e-05\n",
       "[11,] -1.109847e-04\n",
       "\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[[1]]\n",
       "[[1]][[1]]\n",
       "[[1]][[1]]$Granger\n",
       "\n",
       "\tGranger causality H0: y1 do not Granger-cause y2\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 6.4521, df1 = 2, df2 = 40, p-value = 0.003727\n",
       "\n",
       "\n",
       "[[1]][[1]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y1 and y2\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 2.7161, df = 1, p-value = 0.09934\n",
       "\n",
       "\n",
       "\n",
       "[[1]][[2]]\n",
       "[[1]][[2]]$Granger\n",
       "\n",
       "\tGranger causality H0: y2 do not Granger-cause y1\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 0.1523, df1 = 2, df2 = 40, p-value = 0.8592\n",
       "\n",
       "\n",
       "[[1]][[2]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y2 and y1\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 2.7161, df = 1, p-value = 0.09934\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "[[2]]\n",
       "[[2]][[1]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y1\n",
       "                y2\n",
       " [1,]  0.078363566\n",
       " [2,]  0.097042993\n",
       " [3,]  0.106491577\n",
       " [4,] -0.078240711\n",
       " [5,] -0.025550976\n",
       " [6,]  0.018734085\n",
       " [7,]  0.015770727\n",
       " [8,] -0.008003676\n",
       " [9,] -0.005917979\n",
       "[10,]  0.002278810\n",
       "[11,]  0.002654507\n",
       "\n",
       "\n",
       "[[2]][[2]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y2\n",
       "                 y1\n",
       " [1,]  0.000000e+00\n",
       " [2,] -2.090325e-03\n",
       " [3,]  4.147930e-04\n",
       " [4,]  5.727758e-04\n",
       " [5,]  4.009764e-06\n",
       " [6,] -2.768447e-04\n",
       " [7,]  7.148052e-07\n",
       " [8,]  9.740639e-05\n",
       " [9,]  1.203366e-05\n",
       "[10,] -3.913617e-05\n",
       "[11,] -6.818662e-06\n",
       "\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[[1]]\n",
       "[[1]][[1]]\n",
       "[[1]][[1]]$Granger\n",
       "\n",
       "\tGranger causality H0: y1 do not Granger-cause y2\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 1.2832, df1 = 2, df2 = 40, p-value = 0.2883\n",
       "\n",
       "\n",
       "[[1]][[1]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y1 and y2\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 0.017627, df = 1, p-value = 0.8944\n",
       "\n",
       "\n",
       "\n",
       "[[1]][[2]]\n",
       "[[1]][[2]]$Granger\n",
       "\n",
       "\tGranger causality H0: y2 do not Granger-cause y1\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 1.8624, df1 = 2, df2 = 40, p-value = 0.1685\n",
       "\n",
       "\n",
       "[[1]][[2]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y2 and y1\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 0.017627, df = 1, p-value = 0.8944\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "[[2]]\n",
       "[[2]][[1]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y1\n",
       "                y2\n",
       " [1,] -0.006130665\n",
       " [2,]  0.080975452\n",
       " [3,] -0.099064627\n",
       " [4,]  0.030697140\n",
       " [5,]  0.031065837\n",
       " [6,] -0.040973875\n",
       " [7,]  0.026491143\n",
       " [8,] -0.011460432\n",
       " [9,] -0.002930762\n",
       "[10,]  0.013780543\n",
       "[11,] -0.014484067\n",
       "\n",
       "\n",
       "[[2]][[2]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y2\n",
       "                 y1\n",
       " [1,]  0.0000000000\n",
       " [2,]  0.0009923127\n",
       " [3,] -0.0054772536\n",
       " [4,]  0.0052885568\n",
       " [5,] -0.0007368249\n",
       " [6,] -0.0022961889\n",
       " [7,]  0.0022846683\n",
       " [8,] -0.0013071972\n",
       " [9,]  0.0004550914\n",
       "[10,]  0.0003554046\n",
       "[11,] -0.0008809601\n",
       "\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[[1]]\n",
       "[[1]][[1]]\n",
       "[[1]][[1]]$Granger\n",
       "\n",
       "\tGranger causality H0: y1 do not Granger-cause y2\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 0.60519, df1 = 2, df2 = 40, p-value = 0.5509\n",
       "\n",
       "\n",
       "[[1]][[1]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y1 and y2\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 0.17194, df = 1, p-value = 0.6784\n",
       "\n",
       "\n",
       "\n",
       "[[1]][[2]]\n",
       "[[1]][[2]]$Granger\n",
       "\n",
       "\tGranger causality H0: y2 do not Granger-cause y1\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 1.0107, df1 = 2, df2 = 40, p-value = 0.3731\n",
       "\n",
       "\n",
       "[[1]][[2]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y2 and y1\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 0.17194, df = 1, p-value = 0.6784\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "[[2]]\n",
       "[[2]][[1]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y1\n",
       "                 y2\n",
       " [1,] -0.0210875782\n",
       " [2,] -0.0369769122\n",
       " [3,]  0.0154968552\n",
       " [4,]  0.0255958037\n",
       " [5,] -0.0229555840\n",
       " [6,]  0.0012027095\n",
       " [7,]  0.0080307351\n",
       " [8,] -0.0060840681\n",
       " [9,]  0.0026815857\n",
       "[10,] -0.0001033958\n",
       "[11,] -0.0017841071\n",
       "\n",
       "\n",
       "[[2]][[2]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y2\n",
       "                 y1\n",
       " [1,]  0.0000000000\n",
       " [2,]  0.0018193104\n",
       " [3,]  0.0073167656\n",
       " [4,] -0.0089579673\n",
       " [5,]  0.0016186544\n",
       " [6,]  0.0037112074\n",
       " [7,] -0.0034918623\n",
       " [8,]  0.0012379064\n",
       " [9,]  0.0003729919\n",
       "[10,] -0.0010088890\n",
       "[11,]  0.0009039057\n",
       "\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[[1]]\n",
       "[[1]][[1]]\n",
       "[[1]][[1]]$Granger\n",
       "\n",
       "\tGranger causality H0: y1 do not Granger-cause y2\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 1.1385, df1 = 2, df2 = 40, p-value = 0.3305\n",
       "\n",
       "\n",
       "[[1]][[1]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y1 and y2\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 2.8955, df = 1, p-value = 0.08883\n",
       "\n",
       "\n",
       "\n",
       "[[1]][[2]]\n",
       "[[1]][[2]]$Granger\n",
       "\n",
       "\tGranger causality H0: y2 do not Granger-cause y1\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 0.072797, df1 = 2, df2 = 40, p-value = 0.9299\n",
       "\n",
       "\n",
       "[[1]][[2]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y2 and y1\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 2.8955, df = 1, p-value = 0.08883\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "[[2]]\n",
       "[[2]][[1]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y1\n",
       "                y2\n",
       " [1,]  0.070083862\n",
       " [2,]  0.025642767\n",
       " [3,] -0.082173365\n",
       " [4,]  0.001989872\n",
       " [5,]  0.040118317\n",
       " [6,] -0.007079365\n",
       " [7,] -0.016561868\n",
       " [8,]  0.005352092\n",
       " [9,]  0.006327168\n",
       "[10,] -0.003117536\n",
       "[11,] -0.002260731\n",
       "\n",
       "\n",
       "[[2]][[2]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y2\n",
       "                 y1\n",
       " [1,]  0.000000e+00\n",
       " [2,] -1.896397e-03\n",
       " [3,] -2.102965e-03\n",
       " [4,]  1.604203e-03\n",
       " [5,]  8.947346e-04\n",
       " [6,] -8.859306e-04\n",
       " [7,] -2.758519e-04\n",
       " [8,]  4.229241e-04\n",
       " [9,]  6.092708e-05\n",
       "[10,] -1.879797e-04\n",
       "[11,] -8.110571e-07\n",
       "\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[[1]]\n",
       "[[1]][[1]]\n",
       "[[1]][[1]]$Granger\n",
       "\n",
       "\tGranger causality H0: y1 do not Granger-cause y2\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 1.7675, df1 = 2, df2 = 40, p-value = 0.1838\n",
       "\n",
       "\n",
       "[[1]][[1]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y1 and y2\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 4.6109, df = 1, p-value = 0.03177\n",
       "\n",
       "\n",
       "\n",
       "[[1]][[2]]\n",
       "[[1]][[2]]$Granger\n",
       "\n",
       "\tGranger causality H0: y2 do not Granger-cause y1\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 0.93047, df1 = 2, df2 = 40, p-value = 0.4027\n",
       "\n",
       "\n",
       "[[1]][[2]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y2 and y1\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 4.6109, df = 1, p-value = 0.03177\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "[[2]]\n",
       "[[2]][[1]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y1\n",
       "                 y2\n",
       " [1,]  0.0353448845\n",
       " [2,] -0.0042898988\n",
       " [3,] -0.0442086636\n",
       " [4,] -0.0046770498\n",
       " [5,]  0.0211154771\n",
       " [6,]  0.0074449585\n",
       " [7,] -0.0024867403\n",
       " [8,] -0.0040905107\n",
       " [9,] -0.0041066441\n",
       "[10,]  0.0001877894\n",
       "[11,]  0.0034310461\n",
       "\n",
       "\n",
       "[[2]][[2]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y2\n",
       "                 y1\n",
       " [1,]  0.0000000000\n",
       " [2,]  0.0006978687\n",
       " [3,]  0.0086085751\n",
       " [4,]  0.0011413146\n",
       " [5,] -0.0057696640\n",
       " [6,] -0.0018899400\n",
       " [7,]  0.0015608904\n",
       " [8,]  0.0012174947\n",
       " [9,]  0.0005295759\n",
       "[10,] -0.0002512570\n",
       "[11,] -0.0007779936\n",
       "\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[[1]]\n",
       "[[1]][[1]]\n",
       "[[1]][[1]]$Granger\n",
       "\n",
       "\tGranger causality H0: y1 do not Granger-cause y2\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 0.14776, df1 = 2, df2 = 40, p-value = 0.8631\n",
       "\n",
       "\n",
       "[[1]][[1]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y1 and y2\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 4.2637, df = 1, p-value = 0.03894\n",
       "\n",
       "\n",
       "\n",
       "[[1]][[2]]\n",
       "[[1]][[2]]$Granger\n",
       "\n",
       "\tGranger causality H0: y2 do not Granger-cause y1\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 0.01028, df1 = 2, df2 = 40, p-value = 0.9898\n",
       "\n",
       "\n",
       "[[1]][[2]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y2 and y1\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 4.2637, df = 1, p-value = 0.03894\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "[[2]]\n",
       "[[2]][[1]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y1\n",
       "                 y2\n",
       " [1,] -0.0166348771\n",
       " [2,] -0.0150693559\n",
       " [3,] -0.0112602725\n",
       " [4,] -0.0055338618\n",
       " [5,] -0.0032846217\n",
       " [6,] -0.0026640571\n",
       " [7,] -0.0017415985\n",
       " [8,] -0.0009529771\n",
       " [9,] -0.0006238601\n",
       "[10,] -0.0004516596\n",
       "[11,] -0.0002824915\n",
       "\n",
       "\n",
       "[[2]][[2]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y2\n",
       "                 y1\n",
       " [1,]  0.000000e+00\n",
       " [2,] -9.621562e-05\n",
       " [3,]  9.823884e-04\n",
       " [4,]  5.159198e-04\n",
       " [5,]  3.018297e-05\n",
       " [6,]  8.688193e-05\n",
       " [7,]  1.400713e-04\n",
       " [8,]  6.085596e-05\n",
       " [9,]  1.689397e-05\n",
       "[10,]  2.212801e-05\n",
       "[11,]  1.979156e-05\n",
       "\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[[1]]\n",
       "[[1]][[1]]\n",
       "[[1]][[1]]$Granger\n",
       "\n",
       "\tGranger causality H0: y1 do not Granger-cause y2\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 0.54611, df1 = 2, df2 = 40, p-value = 0.5835\n",
       "\n",
       "\n",
       "[[1]][[1]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y1 and y2\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 0.074809, df = 1, p-value = 0.7845\n",
       "\n",
       "\n",
       "\n",
       "[[1]][[2]]\n",
       "[[1]][[2]]$Granger\n",
       "\n",
       "\tGranger causality H0: y2 do not Granger-cause y1\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 0.47375, df1 = 2, df2 = 40, p-value = 0.6261\n",
       "\n",
       "\n",
       "[[1]][[2]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y2 and y1\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 0.074809, df = 1, p-value = 0.7845\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "[[2]]\n",
       "[[2]][[1]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y1\n",
       "                 y2\n",
       " [1,]  5.797382e-04\n",
       " [2,]  1.343455e-03\n",
       " [3,]  3.692294e-03\n",
       " [4,]  9.516470e-04\n",
       " [5,] -4.850143e-04\n",
       " [6,] -3.676530e-04\n",
       " [7,] -9.911605e-05\n",
       " [8,]  1.887854e-05\n",
       " [9,]  3.627038e-05\n",
       "[10,]  1.610929e-05\n",
       "[11,]  1.164464e-07\n",
       "\n",
       "\n",
       "[[2]][[2]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y2\n",
       "                 y1\n",
       " [1,]  0.000000e+00\n",
       " [2,] -9.042499e-03\n",
       " [3,] -4.699633e-03\n",
       " [4,]  3.895610e-04\n",
       " [5,]  1.067293e-03\n",
       " [6,]  4.499776e-04\n",
       " [7,]  3.735419e-05\n",
       " [8,] -8.477796e-05\n",
       " [9,] -5.839446e-05\n",
       "[10,] -1.179485e-05\n",
       "[11,]  7.029314e-06\n",
       "\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[[1]]\n",
       "[[1]][[1]]\n",
       "[[1]][[1]]$Granger\n",
       "\n",
       "\tGranger causality H0: y1 do not Granger-cause y2\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 5.1284, df1 = 2, df2 = 40, p-value = 0.01041\n",
       "\n",
       "\n",
       "[[1]][[1]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y1 and y2\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 0.12946, df = 1, p-value = 0.719\n",
       "\n",
       "\n",
       "\n",
       "[[1]][[2]]\n",
       "[[1]][[2]]$Granger\n",
       "\n",
       "\tGranger causality H0: y2 do not Granger-cause y1\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 0.79266, df1 = 2, df2 = 40, p-value = 0.4596\n",
       "\n",
       "\n",
       "[[1]][[2]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y2 and y1\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 0.12946, df = 1, p-value = 0.719\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "[[2]]\n",
       "[[2]][[1]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y1\n",
       "                 y2\n",
       " [1,] -0.0053339711\n",
       " [2,] -0.0498020620\n",
       " [3,] -0.0524463653\n",
       " [4,]  0.0045881232\n",
       " [5,]  0.0167399007\n",
       " [6,]  0.0068667596\n",
       " [7,]  0.0033779845\n",
       " [8,] -0.0014675162\n",
       " [9,] -0.0034187092\n",
       "[10,] -0.0010654484\n",
       "[11,]  0.0003593281\n",
       "\n",
       "\n",
       "[[2]][[2]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y2\n",
       "                 y1\n",
       " [1,]  0.0000000000\n",
       " [2,] -0.0023316368\n",
       " [3,]  0.0081684808\n",
       " [4,]  0.0025143218\n",
       " [5,] -0.0021888251\n",
       " [6,] -0.0006512846\n",
       " [7,] -0.0004462382\n",
       " [8,] -0.0003122734\n",
       " [9,]  0.0003493642\n",
       "[10,]  0.0002475316\n",
       "[11,] -0.0000109905\n",
       "\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[[1]]\n",
       "[[1]][[1]]\n",
       "[[1]][[1]]$Granger\n",
       "\n",
       "\tGranger causality H0: y1 do not Granger-cause y2\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 0.21367, df1 = 2, df2 = 40, p-value = 0.8085\n",
       "\n",
       "\n",
       "[[1]][[1]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y1 and y2\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 0.25585, df = 1, p-value = 0.613\n",
       "\n",
       "\n",
       "\n",
       "[[1]][[2]]\n",
       "[[1]][[2]]$Granger\n",
       "\n",
       "\tGranger causality H0: y2 do not Granger-cause y1\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 0.35738, df1 = 2, df2 = 40, p-value = 0.7017\n",
       "\n",
       "\n",
       "[[1]][[2]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y2 and y1\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 0.25585, df = 1, p-value = 0.613\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "[[2]]\n",
       "[[2]][[1]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y1\n",
       "                 y2\n",
       " [1,] -2.673040e-02\n",
       " [2,]  3.337111e-02\n",
       " [3,]  3.035267e-02\n",
       " [4,] -2.093324e-02\n",
       " [5,] -1.285846e-02\n",
       " [6,]  1.035943e-02\n",
       " [7,]  3.865490e-03\n",
       " [8,] -4.593211e-03\n",
       " [9,] -7.347455e-04\n",
       "[10,]  1.865192e-03\n",
       "[11,] -6.620248e-05\n",
       "\n",
       "\n",
       "[[2]][[2]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y2\n",
       "                 y1\n",
       " [1,]  0.000000e+00\n",
       " [2,]  6.075678e-03\n",
       " [3,]  2.152135e-03\n",
       " [4,] -3.509197e-03\n",
       " [5,] -8.616035e-04\n",
       " [6,]  1.574180e-03\n",
       " [7,]  1.627621e-04\n",
       " [8,] -6.307831e-04\n",
       " [9,]  3.708014e-05\n",
       "[10,]  2.308771e-04\n",
       "[11,] -5.620002e-05\n",
       "\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[[1]]\n",
       "[[1]][[1]]\n",
       "[[1]][[1]]$Granger\n",
       "\n",
       "\tGranger causality H0: y1 do not Granger-cause y2\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 4.8775, df1 = 2, df2 = 40, p-value = 0.01272\n",
       "\n",
       "\n",
       "[[1]][[1]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y1 and y2\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 7.7259, df = 1, p-value = 0.005444\n",
       "\n",
       "\n",
       "\n",
       "[[1]][[2]]\n",
       "[[1]][[2]]$Granger\n",
       "\n",
       "\tGranger causality H0: y2 do not Granger-cause y1\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 6.2369, df1 = 2, df2 = 40, p-value = 0.004389\n",
       "\n",
       "\n",
       "[[1]][[2]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y2 and y1\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 7.7259, df = 1, p-value = 0.005444\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "[[2]]\n",
       "[[2]][[1]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y1\n",
       "                 y2\n",
       " [1,]  0.1568808987\n",
       " [2,]  0.1096710237\n",
       " [3,] -0.0160909739\n",
       " [4,] -0.0320966019\n",
       " [5,] -0.0483066420\n",
       " [6,] -0.0006760752\n",
       " [7,]  0.0074205709\n",
       " [8,]  0.0170643233\n",
       " [9,]  0.0031436859\n",
       "[10,] -0.0013299935\n",
       "[11,] -0.0055370094\n",
       "\n",
       "\n",
       "[[2]][[2]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y2\n",
       "                 y1\n",
       " [1,]  0.0000000000\n",
       " [2,] -0.0200584223\n",
       " [3,] -0.0006715028\n",
       " [4,] -0.0017913163\n",
       " [5,]  0.0064833802\n",
       " [6,]  0.0011095398\n",
       " [7,]  0.0009692683\n",
       " [8,] -0.0018587104\n",
       " [9,] -0.0006550300\n",
       "[10,] -0.0004537605\n",
       "[11,]  0.0004829488\n",
       "\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[[1]]\n",
       "[[1]][[1]]\n",
       "[[1]][[1]]$Granger\n",
       "\n",
       "\tGranger causality H0: y1 do not Granger-cause y2\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 0.94092, df1 = 2, df2 = 40, p-value = 0.3987\n",
       "\n",
       "\n",
       "[[1]][[1]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y1 and y2\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 0.034083, df = 1, p-value = 0.8535\n",
       "\n",
       "\n",
       "\n",
       "[[1]][[2]]\n",
       "[[1]][[2]]$Granger\n",
       "\n",
       "\tGranger causality H0: y2 do not Granger-cause y1\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 5.4117, df1 = 2, df2 = 40, p-value = 0.008316\n",
       "\n",
       "\n",
       "[[1]][[2]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y2 and y1\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 0.034083, df = 1, p-value = 0.8535\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "[[2]]\n",
       "[[2]][[1]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y1\n",
       "                 y2\n",
       " [1,]  0.0086598165\n",
       " [2,] -0.0179121171\n",
       " [3,] -0.0548028308\n",
       " [4,]  0.0705975624\n",
       " [5,] -0.0142205169\n",
       " [6,] -0.0242888245\n",
       " [7,]  0.0183683655\n",
       " [8,] -0.0044077187\n",
       " [9,]  0.0011436834\n",
       "[10,] -0.0001509064\n",
       "[11,] -0.0029021119\n",
       "\n",
       "\n",
       "[[2]][[2]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y2\n",
       "                 y1\n",
       " [1,]  0.000000e+00\n",
       " [2,]  8.685913e-03\n",
       " [3,] -1.699990e-02\n",
       " [4,]  9.340407e-03\n",
       " [5,]  2.695466e-03\n",
       " [6,] -5.882327e-03\n",
       " [7,]  2.901571e-03\n",
       " [8,] -6.089616e-04\n",
       " [9,] -3.733559e-05\n",
       "[10,]  5.639294e-04\n",
       "[11,] -9.015934e-04\n",
       "\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[[1]]\n",
       "[[1]][[1]]\n",
       "[[1]][[1]]$Granger\n",
       "\n",
       "\tGranger causality H0: y1 do not Granger-cause y2\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 3.5364, df1 = 2, df2 = 40, p-value = 0.03853\n",
       "\n",
       "\n",
       "[[1]][[1]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y1 and y2\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 0.69652, df = 1, p-value = 0.404\n",
       "\n",
       "\n",
       "\n",
       "[[1]][[2]]\n",
       "[[1]][[2]]$Granger\n",
       "\n",
       "\tGranger causality H0: y2 do not Granger-cause y1\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 0.31297, df1 = 2, df2 = 40, p-value = 0.733\n",
       "\n",
       "\n",
       "[[1]][[2]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y2 and y1\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 0.69652, df = 1, p-value = 0.404\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "[[2]]\n",
       "[[2]][[1]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y1\n",
       "                 y2\n",
       " [1,] -2.979481e-02\n",
       " [2,] -8.041679e-02\n",
       " [3,]  6.958993e-02\n",
       " [4,]  1.489466e-02\n",
       " [5,] -4.733313e-02\n",
       " [6,]  2.593392e-02\n",
       " [7,] -7.061473e-05\n",
       " [8,] -1.136751e-02\n",
       " [9,]  1.193434e-02\n",
       "[10,] -6.251137e-03\n",
       "[11,] -1.120401e-03\n",
       "\n",
       "\n",
       "[[2]][[2]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y2\n",
       "                 y1\n",
       " [1,]  0.0000000000\n",
       " [2,] -0.0404928689\n",
       " [3,]  0.0390175103\n",
       " [4,] -0.0013156930\n",
       " [5,] -0.0216092944\n",
       " [6,]  0.0177062068\n",
       " [7,] -0.0046005032\n",
       " [8,] -0.0045325780\n",
       " [9,]  0.0071139371\n",
       "[10,] -0.0047474889\n",
       "[11,]  0.0004846754\n",
       "\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[[1]]\n",
       "[[1]][[1]]\n",
       "[[1]][[1]]$Granger\n",
       "\n",
       "\tGranger causality H0: y1 do not Granger-cause y2\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 0.22556, df1 = 2, df2 = 40, p-value = 0.7991\n",
       "\n",
       "\n",
       "[[1]][[1]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y1 and y2\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 0.50844, df = 1, p-value = 0.4758\n",
       "\n",
       "\n",
       "\n",
       "[[1]][[2]]\n",
       "[[1]][[2]]$Granger\n",
       "\n",
       "\tGranger causality H0: y2 do not Granger-cause y1\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 0.17163, df1 = 2, df2 = 40, p-value = 0.8429\n",
       "\n",
       "\n",
       "[[1]][[2]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y2 and y1\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 0.50844, df = 1, p-value = 0.4758\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "[[2]]\n",
       "[[2]][[1]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y1\n",
       "                 y2\n",
       " [1,] -1.153313e-02\n",
       " [2,]  4.583665e-03\n",
       " [3,]  8.658650e-03\n",
       " [4,] -5.419306e-03\n",
       " [5,] -1.000543e-03\n",
       " [6,]  1.287946e-03\n",
       " [7,] -8.339275e-04\n",
       " [8,]  1.008723e-03\n",
       " [9,] -7.294425e-05\n",
       "[10,] -7.157121e-04\n",
       "[11,]  4.195769e-04\n",
       "\n",
       "\n",
       "[[2]][[2]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y2\n",
       "                 y1\n",
       " [1,]  0.0000000000\n",
       " [2,] -0.0185920945\n",
       " [3,]  0.0399390999\n",
       " [4,] -0.0102960390\n",
       " [5,] -0.0161619194\n",
       " [6,]  0.0114832251\n",
       " [7,] -0.0025085011\n",
       " [8,] -0.0004700521\n",
       " [9,]  0.0030150131\n",
       "[10,] -0.0033411278\n",
       "[11,]  0.0005589427\n",
       "\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[[1]]\n",
       "[[1]][[1]]\n",
       "[[1]][[1]]$Granger\n",
       "\n",
       "\tGranger causality H0: y1 do not Granger-cause y2\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 0.1286, df1 = 2, df2 = 40, p-value = 0.8797\n",
       "\n",
       "\n",
       "[[1]][[1]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y1 and y2\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 7.5113e-05, df = 1, p-value = 0.9931\n",
       "\n",
       "\n",
       "\n",
       "[[1]][[2]]\n",
       "[[1]][[2]]$Granger\n",
       "\n",
       "\tGranger causality H0: y2 do not Granger-cause y1\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 0.30739, df1 = 2, df2 = 40, p-value = 0.7371\n",
       "\n",
       "\n",
       "[[1]][[2]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y2 and y1\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 7.5113e-05, df = 1, p-value = 0.9931\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "[[2]]\n",
       "[[2]][[1]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y1\n",
       "                 y2\n",
       " [1,] -6.390028e-05\n",
       " [2,]  2.960940e-03\n",
       " [3,] -4.669622e-04\n",
       " [4,]  2.642040e-04\n",
       " [5,]  9.161452e-04\n",
       " [6,] -3.681112e-04\n",
       " [7,]  2.300059e-04\n",
       " [8,]  2.348515e-04\n",
       " [9,] -1.698651e-04\n",
       "[10,]  1.325537e-04\n",
       "[11,]  3.807201e-05\n",
       "\n",
       "\n",
       "[[2]][[2]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y2\n",
       "                 y1\n",
       " [1,]  0.0000000000\n",
       " [2,]  0.0412697681\n",
       " [3,] -0.0494956281\n",
       " [4,]  0.0129479941\n",
       " [5,]  0.0080908601\n",
       " [6,] -0.0180861459\n",
       " [7,]  0.0092642589\n",
       " [8,] -0.0005078221\n",
       " [9,] -0.0055126587\n",
       "[10,]  0.0044673910\n",
       "[11,] -0.0015670057\n",
       "\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[[1]]\n",
       "[[1]][[1]]\n",
       "[[1]][[1]]$Granger\n",
       "\n",
       "\tGranger causality H0: y1 do not Granger-cause y2\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 0.050906, df1 = 2, df2 = 40, p-value = 0.9504\n",
       "\n",
       "\n",
       "[[1]][[1]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y1 and y2\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 0.044612, df = 1, p-value = 0.8327\n",
       "\n",
       "\n",
       "\n",
       "[[1]][[2]]\n",
       "[[1]][[2]]$Granger\n",
       "\n",
       "\tGranger causality H0: y2 do not Granger-cause y1\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 0.072732, df1 = 2, df2 = 40, p-value = 0.93\n",
       "\n",
       "\n",
       "[[1]][[2]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y2 and y1\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 0.044612, df = 1, p-value = 0.8327\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "[[2]]\n",
       "[[2]][[1]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y1\n",
       "                 y2\n",
       " [1,] -4.583256e-04\n",
       " [2,] -5.714054e-04\n",
       " [3,] -7.021123e-04\n",
       " [4,]  1.484339e-04\n",
       " [5,] -4.241339e-05\n",
       " [6,] -1.024306e-04\n",
       " [7,]  1.174048e-04\n",
       " [8,] -3.969554e-05\n",
       " [9,] -1.991119e-05\n",
       "[10,]  4.153039e-05\n",
       "[11,] -2.603162e-05\n",
       "\n",
       "\n",
       "[[2]][[2]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y2\n",
       "                 y1\n",
       " [1,]  0.0000000000\n",
       " [2,] -0.0139426834\n",
       " [3,] -0.0099348656\n",
       " [4,]  0.0040525221\n",
       " [5,] -0.0028156846\n",
       " [6,] -0.0006206693\n",
       " [7,]  0.0020846055\n",
       " [8,] -0.0013677407\n",
       " [9,]  0.0002263545\n",
       "[10,]  0.0005636796\n",
       "[11,] -0.0006150914\n",
       "\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[[1]]\n",
       "[[1]][[1]]\n",
       "[[1]][[1]]$Granger\n",
       "\n",
       "\tGranger causality H0: y1 do not Granger-cause y2\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 0.33956, df1 = 2, df2 = 40, p-value = 0.7141\n",
       "\n",
       "\n",
       "[[1]][[1]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y1 and y2\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 0.59666, df = 1, p-value = 0.4399\n",
       "\n",
       "\n",
       "\n",
       "[[1]][[2]]\n",
       "[[1]][[2]]$Granger\n",
       "\n",
       "\tGranger causality H0: y2 do not Granger-cause y1\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 0.035971, df1 = 2, df2 = 40, p-value = 0.9647\n",
       "\n",
       "\n",
       "[[1]][[2]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y2 and y1\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 0.59666, df = 1, p-value = 0.4399\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "[[2]]\n",
       "[[2]][[1]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y1\n",
       "                 y2\n",
       " [1,]  0.0139783828\n",
       " [2,]  0.0183492318\n",
       " [3,]  0.0131141675\n",
       " [4,] -0.0066119390\n",
       " [5,] -0.0012093949\n",
       " [6,]  0.0008218609\n",
       " [7,] -0.0022211732\n",
       " [8,]  0.0011447457\n",
       " [9,]  0.0004259822\n",
       "[10,] -0.0006996187\n",
       "[11,]  0.0004840044\n",
       "\n",
       "\n",
       "[[2]][[2]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y2\n",
       "                 y1\n",
       " [1,]  0.0000000000\n",
       " [2,] -0.0137164602\n",
       " [3,]  0.0155473312\n",
       " [4,] -0.0011613710\n",
       " [5,] -0.0045948213\n",
       " [6,]  0.0047532801\n",
       " [7,] -0.0023214409\n",
       " [8,] -0.0007468103\n",
       " [9,]  0.0017767952\n",
       "[10,] -0.0011401865\n",
       "[11,]  0.0001259634\n",
       "\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[[1]]\n",
       "[[1]][[1]]\n",
       "[[1]][[1]]$Granger\n",
       "\n",
       "\tGranger causality H0: y1 do not Granger-cause y2\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 0.84534, df1 = 2, df2 = 40, p-value = 0.4369\n",
       "\n",
       "\n",
       "[[1]][[1]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y1 and y2\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 1.2149, df = 1, p-value = 0.2704\n",
       "\n",
       "\n",
       "\n",
       "[[1]][[2]]\n",
       "[[1]][[2]]$Granger\n",
       "\n",
       "\tGranger causality H0: y2 do not Granger-cause y1\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 1.6856, df1 = 2, df2 = 40, p-value = 0.1982\n",
       "\n",
       "\n",
       "[[1]][[2]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y2 and y1\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 1.2149, df = 1, p-value = 0.2704\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "[[2]]\n",
       "[[2]][[1]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y1\n",
       "                 y2\n",
       " [1,] -0.0576095916\n",
       " [2,]  0.0484212703\n",
       " [3,] -0.0246275754\n",
       " [4,] -0.0024809321\n",
       " [5,]  0.0174881800\n",
       " [6,] -0.0138301831\n",
       " [7,]  0.0025308642\n",
       " [8,]  0.0044927840\n",
       " [9,] -0.0049101549\n",
       "[10,]  0.0021241736\n",
       "[11,]  0.0004931497\n",
       "\n",
       "\n",
       "[[2]][[2]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y2\n",
       "                y1\n",
       " [1,]  0.000000000\n",
       " [2,]  0.015312928\n",
       " [3,] -0.107907951\n",
       " [4,]  0.076403574\n",
       " [5,]  0.013749969\n",
       " [6,] -0.045013008\n",
       " [7,]  0.024794336\n",
       " [8,] -0.001306060\n",
       " [9,] -0.008927528\n",
       "[10,]  0.009125187\n",
       "[11,] -0.004163034\n",
       "\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[[1]]\n",
       "[[1]][[1]]\n",
       "[[1]][[1]]$Granger\n",
       "\n",
       "\tGranger causality H0: y1 do not Granger-cause y2\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 0.60425, df1 = 2, df2 = 40, p-value = 0.5514\n",
       "\n",
       "\n",
       "[[1]][[1]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y1 and y2\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 0.3928, df = 1, p-value = 0.5308\n",
       "\n",
       "\n",
       "\n",
       "[[1]][[2]]\n",
       "[[1]][[2]]$Granger\n",
       "\n",
       "\tGranger causality H0: y2 do not Granger-cause y1\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 0.26815, df1 = 2, df2 = 40, p-value = 0.7662\n",
       "\n",
       "\n",
       "[[1]][[2]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y2 and y1\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 0.3928, df = 1, p-value = 0.5308\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "[[2]]\n",
       "[[2]][[1]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y1\n",
       "                y2\n",
       " [1,] -0.035395219\n",
       " [2,] -0.035406384\n",
       " [3,]  0.049130981\n",
       " [4,] -0.025511786\n",
       " [5,] -0.003153488\n",
       " [6,]  0.016839845\n",
       " [7,] -0.013869697\n",
       " [8,]  0.003648410\n",
       " [9,]  0.004193285\n",
       "[10,] -0.005873720\n",
       "[11,]  0.003148961\n",
       "\n",
       "\n",
       "[[2]][[2]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y2\n",
       "                y1\n",
       " [1,]  0.000000000\n",
       " [2,] -0.038591069\n",
       " [3,]  0.016259033\n",
       " [4,]  0.005960022\n",
       " [5,] -0.014189631\n",
       " [6,]  0.009888841\n",
       " [7,] -0.001411534\n",
       " [8,] -0.004091282\n",
       " [9,]  0.004558268\n",
       "[10,] -0.001986784\n",
       "[11,] -0.000648826\n",
       "\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[[1]]\n",
       "[[1]][[1]]\n",
       "[[1]][[1]]$Granger\n",
       "\n",
       "\tGranger causality H0: y1 do not Granger-cause y2\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 0.090058, df1 = 2, df2 = 40, p-value = 0.9141\n",
       "\n",
       "\n",
       "[[1]][[1]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y1 and y2\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 3.9334, df = 1, p-value = 0.04734\n",
       "\n",
       "\n",
       "\n",
       "[[1]][[2]]\n",
       "[[1]][[2]]$Granger\n",
       "\n",
       "\tGranger causality H0: y2 do not Granger-cause y1\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 0.29394, df1 = 2, df2 = 40, p-value = 0.7469\n",
       "\n",
       "\n",
       "[[1]][[2]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y2 and y1\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 3.9334, df = 1, p-value = 0.04734\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "[[2]]\n",
       "[[2]][[1]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y1\n",
       "                y2\n",
       " [1,] -0.105085570\n",
       " [2,]  0.138705164\n",
       " [3,] -0.093923709\n",
       " [4,]  0.012295785\n",
       " [5,]  0.052029056\n",
       " [6,] -0.068746408\n",
       " [7,]  0.043322430\n",
       " [8,] -0.003398047\n",
       " [9,] -0.024187870\n",
       "[10,]  0.028629058\n",
       "[11,] -0.015893069\n",
       "\n",
       "\n",
       "[[2]][[2]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y2\n",
       "                y1\n",
       " [1,]  0.000000000\n",
       " [2,] -0.039476583\n",
       " [3,]  0.053105421\n",
       " [4,] -0.022429714\n",
       " [5,] -0.024249220\n",
       " [6,]  0.049655807\n",
       " [7,] -0.039834737\n",
       " [8,]  0.008707723\n",
       " [9,]  0.019072704\n",
       "[10,] -0.027675898\n",
       "[11,]  0.017777564\n",
       "\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[[1]]\n",
       "[[1]][[1]]\n",
       "[[1]][[1]]$Granger\n",
       "\n",
       "\tGranger causality H0: y1 do not Granger-cause y2\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 2.3354, df1 = 2, df2 = 40, p-value = 0.1098\n",
       "\n",
       "\n",
       "[[1]][[1]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y1 and y2\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 0.16208, df = 1, p-value = 0.6873\n",
       "\n",
       "\n",
       "\n",
       "[[1]][[2]]\n",
       "[[1]][[2]]$Granger\n",
       "\n",
       "\tGranger causality H0: y2 do not Granger-cause y1\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 0.19329, df1 = 2, df2 = 40, p-value = 0.825\n",
       "\n",
       "\n",
       "[[1]][[2]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y2 and y1\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 0.16208, df = 1, p-value = 0.6873\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "[[2]]\n",
       "[[2]][[1]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y1\n",
       "                 y2\n",
       " [1,]  0.0058887794\n",
       " [2,] -0.0219879796\n",
       " [3,] -0.0316795457\n",
       " [4,]  0.0106731675\n",
       " [5,]  0.0223303238\n",
       " [6,] -0.0009330474\n",
       " [7,] -0.0112450014\n",
       " [8,] -0.0025839101\n",
       " [9,]  0.0043678784\n",
       "[10,]  0.0026176941\n",
       "[11,] -0.0011053035\n",
       "\n",
       "\n",
       "[[2]][[2]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y2\n",
       "                 y1\n",
       " [1,]  0.0000000000\n",
       " [2,]  0.0197859324\n",
       " [3,]  0.0085047007\n",
       " [4,] -0.0122471668\n",
       " [5,] -0.0076412625\n",
       " [6,]  0.0045614341\n",
       " [7,]  0.0048436663\n",
       " [8,] -0.0007325248\n",
       " [9,] -0.0024466784\n",
       "[10,] -0.0004955515\n",
       "[11,]  0.0009701684\n",
       "\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[[1]]\n",
       "[[1]][[1]]\n",
       "[[1]][[1]]$Granger\n",
       "\n",
       "\tGranger causality H0: y1 do not Granger-cause y2\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 0.07351, df1 = 2, df2 = 40, p-value = 0.9293\n",
       "\n",
       "\n",
       "[[1]][[1]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y1 and y2\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 7.6804, df = 1, p-value = 0.005582\n",
       "\n",
       "\n",
       "\n",
       "[[1]][[2]]\n",
       "[[1]][[2]]$Granger\n",
       "\n",
       "\tGranger causality H0: y2 do not Granger-cause y1\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 1.1447, df1 = 2, df2 = 40, p-value = 0.3285\n",
       "\n",
       "\n",
       "[[1]][[2]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y2 and y1\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 7.6804, df = 1, p-value = 0.005582\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "[[2]]\n",
       "[[2]][[1]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y1\n",
       "                 y2\n",
       " [1,] -0.0244088741\n",
       " [2,] -0.0142906696\n",
       " [3,] -0.0092152338\n",
       " [4,] -0.0070896362\n",
       " [5,] -0.0044715720\n",
       " [6,] -0.0026449434\n",
       " [7,] -0.0018247648\n",
       " [8,] -0.0012439608\n",
       " [9,] -0.0007704212\n",
       "[10,] -0.0004953600\n",
       "[11,] -0.0003357527\n",
       "\n",
       "\n",
       "[[2]][[2]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y2\n",
       "                y1\n",
       " [1,] 0.0000000000\n",
       " [2,] 0.0156419774\n",
       " [3,] 0.0357493974\n",
       " [4,] 0.0160592386\n",
       " [5,] 0.0050410905\n",
       " [6,] 0.0065304115\n",
       " [7,] 0.0050959152\n",
       " [8,] 0.0022213149\n",
       " [9,] 0.0014471524\n",
       "[10,] 0.0012549739\n",
       "[11,] 0.0007509063\n",
       "\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[[1]]\n",
       "[[1]][[1]]\n",
       "[[1]][[1]]$Granger\n",
       "\n",
       "\tGranger causality H0: y1 do not Granger-cause y2\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 0.12298, df1 = 2, df2 = 40, p-value = 0.8846\n",
       "\n",
       "\n",
       "[[1]][[1]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y1 and y2\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 1.1721, df = 1, p-value = 0.279\n",
       "\n",
       "\n",
       "\n",
       "[[1]][[2]]\n",
       "[[1]][[2]]$Granger\n",
       "\n",
       "\tGranger causality H0: y2 do not Granger-cause y1\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 4.2907, df1 = 2, df2 = 40, p-value = 0.0205\n",
       "\n",
       "\n",
       "[[1]][[2]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y2 and y1\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 1.1721, df = 1, p-value = 0.279\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "[[2]]\n",
       "[[2]][[1]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y1\n",
       "                 y2\n",
       " [1,]  2.393442e-03\n",
       " [2,]  6.949787e-04\n",
       " [3,] -3.985533e-04\n",
       " [4,]  8.299318e-06\n",
       " [5,]  1.357457e-04\n",
       " [6,] -4.948525e-05\n",
       " [7,] -4.958725e-05\n",
       " [8,]  1.614628e-05\n",
       " [9,]  8.299980e-06\n",
       "[10,] -7.876350e-06\n",
       "[11,] -1.063969e-06\n",
       "\n",
       "\n",
       "[[2]][[2]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y2\n",
       "                 y1\n",
       " [1,]  0.0000000000\n",
       " [2,] -0.1231163323\n",
       " [3,] -0.0565399243\n",
       " [4,]  0.0115433282\n",
       " [5,]  0.0018574882\n",
       " [6,] -0.0066879239\n",
       " [7,]  0.0014051083\n",
       " [8,]  0.0028188713\n",
       " [9,] -0.0003579063\n",
       "[10,] -0.0005012625\n",
       "[11,]  0.0003207122\n",
       "\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[[1]]\n",
       "[[1]][[1]]\n",
       "[[1]][[1]]$Granger\n",
       "\n",
       "\tGranger causality H0: y1 do not Granger-cause y2\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 5.0203, df1 = 2, df2 = 40, p-value = 0.01134\n",
       "\n",
       "\n",
       "[[1]][[1]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y1 and y2\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 0.00035453, df = 1, p-value = 0.985\n",
       "\n",
       "\n",
       "\n",
       "[[1]][[2]]\n",
       "[[1]][[2]]$Granger\n",
       "\n",
       "\tGranger causality H0: y2 do not Granger-cause y1\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 0.43562, df1 = 2, df2 = 40, p-value = 0.6499\n",
       "\n",
       "\n",
       "[[1]][[2]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y2 and y1\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 0.00035453, df = 1, p-value = 0.985\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "[[2]]\n",
       "[[2]][[1]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y1\n",
       "                 y2\n",
       " [1,]  0.0002794416\n",
       " [2,] -0.0480123878\n",
       " [3,] -0.0219359086\n",
       " [4,]  0.0109919220\n",
       " [5,]  0.0099322377\n",
       " [6,]  0.0015918491\n",
       " [7,] -0.0005910777\n",
       " [8,] -0.0008482821\n",
       " [9,] -0.0008282068\n",
       "[10,] -0.0002262998\n",
       "[11,]  0.0002198682\n",
       "\n",
       "\n",
       "[[2]][[2]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y2\n",
       "                 y1\n",
       " [1,]  0.0000000000\n",
       " [2,] -0.0127275063\n",
       " [3,]  0.0297504465\n",
       " [4,]  0.0203733094\n",
       " [5,] -0.0048853809\n",
       " [6,] -0.0071946614\n",
       " [7,] -0.0016102574\n",
       " [8,]  0.0001609362\n",
       " [9,]  0.0004246063\n",
       "[10,]  0.0005764942\n",
       "[11,]  0.0002479218\n",
       "\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[[1]]\n",
       "[[1]][[1]]\n",
       "[[1]][[1]]$Granger\n",
       "\n",
       "\tGranger causality H0: y1 do not Granger-cause y2\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 0.81114, df1 = 2, df2 = 40, p-value = 0.4515\n",
       "\n",
       "\n",
       "[[1]][[1]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y1 and y2\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 0.36905, df = 1, p-value = 0.5435\n",
       "\n",
       "\n",
       "\n",
       "[[1]][[2]]\n",
       "[[1]][[2]]$Granger\n",
       "\n",
       "\tGranger causality H0: y2 do not Granger-cause y1\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 4.4455, df1 = 2, df2 = 40, p-value = 0.01806\n",
       "\n",
       "\n",
       "[[1]][[2]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y2 and y1\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 0.36905, df = 1, p-value = 0.5435\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "[[2]]\n",
       "[[2]][[1]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y1\n",
       "                 y2\n",
       " [1,] -0.0312731464\n",
       " [2,] -0.0745063119\n",
       " [3,]  0.0047529714\n",
       " [4,]  0.0228763228\n",
       " [5,] -0.0032719392\n",
       " [6,] -0.0074513557\n",
       " [7,]  0.0015614130\n",
       " [8,]  0.0023830228\n",
       " [9,] -0.0006726943\n",
       "[10,] -0.0007513107\n",
       "[11,]  0.0002721808\n",
       "\n",
       "\n",
       "[[2]][[2]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y2\n",
       "                 y1\n",
       " [1,]  0.0000000000\n",
       " [2,]  0.1106834177\n",
       " [3,] -0.0190417278\n",
       " [4,] -0.0334787202\n",
       " [5,]  0.0082533922\n",
       " [6,]  0.0106748639\n",
       " [7,] -0.0034369414\n",
       " [8,] -0.0033375416\n",
       " [9,]  0.0013585602\n",
       "[10,]  0.0010256501\n",
       "[11,] -0.0005182996\n",
       "\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[[1]]\n",
       "[[1]][[1]]\n",
       "[[1]][[1]]$Granger\n",
       "\n",
       "\tGranger causality H0: y1 do not Granger-cause y2\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 0.07748, df1 = 2, df2 = 40, p-value = 0.9256\n",
       "\n",
       "\n",
       "[[1]][[1]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y1 and y2\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 3.4211, df = 1, p-value = 0.06437\n",
       "\n",
       "\n",
       "\n",
       "[[1]][[2]]\n",
       "[[1]][[2]]$Granger\n",
       "\n",
       "\tGranger causality H0: y2 do not Granger-cause y1\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 0.12421, df1 = 2, df2 = 40, p-value = 0.8835\n",
       "\n",
       "\n",
       "[[1]][[2]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y2 and y1\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 3.4211, df = 1, p-value = 0.06437\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "[[2]]\n",
       "[[2]][[1]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y1\n",
       "                 y2\n",
       " [1,]  1.141128e-01\n",
       " [2,] -2.427940e-03\n",
       " [3,]  2.105769e-02\n",
       " [4,] -4.038503e-03\n",
       " [5,] -8.145532e-03\n",
       " [6,]  2.421846e-03\n",
       " [7,]  1.932755e-03\n",
       " [8,] -9.307473e-04\n",
       " [9,] -3.635543e-04\n",
       "[10,]  2.954502e-04\n",
       "[11,]  4.761636e-05\n",
       "\n",
       "\n",
       "[[2]][[2]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y2\n",
       "                 y1\n",
       " [1,]  0.000000e+00\n",
       " [2,] -1.073367e-02\n",
       " [3,] -1.779275e-02\n",
       " [4,]  6.382453e-03\n",
       " [5,]  4.871147e-03\n",
       " [6,] -2.460116e-03\n",
       " [7,] -9.488868e-04\n",
       " [8,]  7.822027e-04\n",
       " [9,]  1.258858e-04\n",
       "[10,] -2.199111e-04\n",
       "[11,]  1.904798e-06\n",
       "\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[[1]]\n",
       "[[1]][[1]]\n",
       "[[1]][[1]]$Granger\n",
       "\n",
       "\tGranger causality H0: y1 do not Granger-cause y2\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 2.3952, df1 = 2, df2 = 40, p-value = 0.1041\n",
       "\n",
       "\n",
       "[[1]][[1]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y1 and y2\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 0.22935, df = 1, p-value = 0.632\n",
       "\n",
       "\n",
       "\n",
       "[[1]][[2]]\n",
       "[[1]][[2]]$Granger\n",
       "\n",
       "\tGranger causality H0: y2 do not Granger-cause y1\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 6.843, df1 = 2, df2 = 40, p-value = 0.00278\n",
       "\n",
       "\n",
       "[[1]][[2]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y2 and y1\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 0.22935, df = 1, p-value = 0.632\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "[[2]]\n",
       "[[2]][[1]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y1\n",
       "                 y2\n",
       " [1,]  0.0211853680\n",
       " [2,] -0.0006971279\n",
       " [3,] -0.0933895582\n",
       " [4,]  0.1042061218\n",
       " [5,] -0.0311350939\n",
       " [6,] -0.0211703212\n",
       " [7,]  0.0333997817\n",
       " [8,] -0.0393332115\n",
       " [9,]  0.0367291081\n",
       "[10,] -0.0135252902\n",
       "[11,] -0.0136358497\n",
       "\n",
       "\n",
       "[[2]][[2]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y2\n",
       "               y1\n",
       " [1,]  0.00000000\n",
       " [2,]  0.10401463\n",
       " [3,] -0.05820516\n",
       " [4,] -0.04664983\n",
       " [5,]  0.05919241\n",
       " [6,] -0.02507554\n",
       " [7,]  0.01625165\n",
       " [8,] -0.01047758\n",
       " [9,] -0.01328261\n",
       "[10,]  0.02835429\n",
       "[11,] -0.02006116\n",
       "\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[[1]]\n",
       "[[1]][[1]]\n",
       "[[1]][[1]]$Granger\n",
       "\n",
       "\tGranger causality H0: y1 do not Granger-cause y2\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 4.4736, df1 = 2, df2 = 40, p-value = 0.01765\n",
       "\n",
       "\n",
       "[[1]][[1]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y1 and y2\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 0.90622, df = 1, p-value = 0.3411\n",
       "\n",
       "\n",
       "\n",
       "[[1]][[2]]\n",
       "[[1]][[2]]$Granger\n",
       "\n",
       "\tGranger causality H0: y2 do not Granger-cause y1\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 1.0147, df1 = 2, df2 = 40, p-value = 0.3716\n",
       "\n",
       "\n",
       "[[1]][[2]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y2 and y1\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 0.90622, df = 1, p-value = 0.3411\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "[[2]]\n",
       "[[2]][[1]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y1\n",
       "                 y2\n",
       " [1,] -5.976553e-03\n",
       " [2,] -1.776267e-03\n",
       " [3,] -1.947377e-02\n",
       " [4,] -1.718498e-02\n",
       " [5,] -4.195423e-03\n",
       " [6,]  3.604792e-03\n",
       " [7,] -5.483059e-06\n",
       " [8,] -5.624350e-03\n",
       " [9,] -5.041408e-03\n",
       "[10,] -3.762738e-04\n",
       "[11,]  1.911192e-03\n",
       "\n",
       "\n",
       "[[2]][[2]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y2\n",
       "                y1\n",
       " [1,]  0.000000000\n",
       " [2,]  0.008844547\n",
       " [3,] -0.011991923\n",
       " [4,] -0.016244982\n",
       " [5,] -0.005395408\n",
       " [6,]  0.004825091\n",
       " [7,]  0.003144898\n",
       " [8,] -0.003649624\n",
       " [9,] -0.005256667\n",
       "[10,] -0.001102018\n",
       "[11,]  0.002215679\n",
       "\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[[1]]\n",
       "[[1]][[1]]\n",
       "[[1]][[1]]$Granger\n",
       "\n",
       "\tGranger causality H0: y1 do not Granger-cause y2\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 3.4344, df1 = 2, df2 = 40, p-value = 0.04203\n",
       "\n",
       "\n",
       "[[1]][[1]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y1 and y2\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 0.97468, df = 1, p-value = 0.3235\n",
       "\n",
       "\n",
       "\n",
       "[[1]][[2]]\n",
       "[[1]][[2]]$Granger\n",
       "\n",
       "\tGranger causality H0: y2 do not Granger-cause y1\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 1.6042, df1 = 2, df2 = 40, p-value = 0.2137\n",
       "\n",
       "\n",
       "[[1]][[2]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y2 and y1\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 0.97468, df = 1, p-value = 0.3235\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "[[2]]\n",
       "[[2]][[1]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y1\n",
       "                 y2\n",
       " [1,]  0.0018870996\n",
       " [2,] -0.0024681227\n",
       " [3,]  0.0007164136\n",
       " [4,]  0.0027903109\n",
       " [5,]  0.0020681860\n",
       " [6,] -0.0001417288\n",
       " [7,] -0.0012170059\n",
       " [8,] -0.0005580130\n",
       " [9,]  0.0005174249\n",
       "[10,]  0.0007436314\n",
       "[11,]  0.0001650703\n",
       "\n",
       "\n",
       "[[2]][[2]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y2\n",
       "                 y1\n",
       " [1,]  0.0000000000\n",
       " [2,] -0.0081235331\n",
       " [3,]  0.0210427841\n",
       " [4,]  0.0268253972\n",
       " [5,]  0.0099050389\n",
       " [6,] -0.0078520945\n",
       " [7,] -0.0094517733\n",
       " [8,]  0.0002788632\n",
       " [9,]  0.0070875121\n",
       "[10,]  0.0047851581\n",
       "[11,] -0.0013291547\n",
       "\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[[1]]\n",
       "[[1]][[1]]\n",
       "[[1]][[1]]$Granger\n",
       "\n",
       "\tGranger causality H0: y1 do not Granger-cause y2\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 0.57076, df1 = 2, df2 = 40, p-value = 0.5696\n",
       "\n",
       "\n",
       "[[1]][[1]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y1 and y2\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 1.4093, df = 1, p-value = 0.2352\n",
       "\n",
       "\n",
       "\n",
       "[[1]][[2]]\n",
       "[[1]][[2]]$Granger\n",
       "\n",
       "\tGranger causality H0: y2 do not Granger-cause y1\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 2.452, df1 = 2, df2 = 40, p-value = 0.09897\n",
       "\n",
       "\n",
       "[[1]][[2]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y2 and y1\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 1.4093, df = 1, p-value = 0.2352\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "[[2]]\n",
       "[[2]][[1]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y1\n",
       "                 y2\n",
       " [1,]  0.0215946289\n",
       " [2,] -0.0013161168\n",
       " [3,] -0.0148350383\n",
       " [4,] -0.0052949044\n",
       " [5,]  0.0048789527\n",
       " [6,]  0.0035911326\n",
       " [7,] -0.0014087358\n",
       " [8,] -0.0020949238\n",
       " [9,]  0.0001041116\n",
       "[10,]  0.0010243458\n",
       "[11,]  0.0002284921\n",
       "\n",
       "\n",
       "[[2]][[2]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y2\n",
       "                 y1\n",
       " [1,]  0.0000000000\n",
       " [2,]  0.0106113712\n",
       " [3,] -0.0242627326\n",
       " [4,] -0.0314835342\n",
       " [5,] -0.0068603684\n",
       " [6,]  0.0090581312\n",
       " [7,]  0.0042945940\n",
       " [8,] -0.0035501070\n",
       " [9,] -0.0030858753\n",
       "[10,]  0.0008458853\n",
       "[11,]  0.0017008659\n",
       "\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[[1]]\n",
       "[[1]][[1]]\n",
       "[[1]][[1]]$Granger\n",
       "\n",
       "\tGranger causality H0: y1 do not Granger-cause y2\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 0.4473, df1 = 2, df2 = 40, p-value = 0.6425\n",
       "\n",
       "\n",
       "[[1]][[1]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y1 and y2\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 0.76627, df = 1, p-value = 0.3814\n",
       "\n",
       "\n",
       "\n",
       "[[1]][[2]]\n",
       "[[1]][[2]]$Granger\n",
       "\n",
       "\tGranger causality H0: y2 do not Granger-cause y1\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 0.34189, df1 = 2, df2 = 40, p-value = 0.7125\n",
       "\n",
       "\n",
       "[[1]][[2]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y2 and y1\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 0.76627, df = 1, p-value = 0.3814\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "[[2]]\n",
       "[[2]][[1]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y1\n",
       "                 y2\n",
       " [1,] -4.620027e-02\n",
       " [2,]  4.306575e-02\n",
       " [3,]  3.248012e-02\n",
       " [4,] -1.590631e-02\n",
       " [5,] -1.880688e-02\n",
       " [6,]  8.229412e-04\n",
       " [7,]  8.048730e-03\n",
       " [8,]  2.741067e-03\n",
       " [9,] -2.263436e-03\n",
       "[10,] -2.109499e-03\n",
       "[11,]  8.782955e-05\n",
       "\n",
       "\n",
       "[[2]][[2]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y2\n",
       "                 y1\n",
       " [1,]  0.000000e+00\n",
       " [2,] -3.062323e-03\n",
       " [3,] -1.526315e-02\n",
       " [4,] -3.117116e-03\n",
       " [5,]  7.698952e-03\n",
       " [6,]  4.008399e-03\n",
       " [7,] -2.060072e-03\n",
       " [8,] -2.477708e-03\n",
       " [9,] -7.911246e-05\n",
       "[10,]  1.022710e-03\n",
       "[11,]  4.543459e-04\n",
       "\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[[1]]\n",
       "[[1]][[1]]\n",
       "[[1]][[1]]$Granger\n",
       "\n",
       "\tGranger causality H0: y1 do not Granger-cause y2\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 0.22049, df1 = 2, df2 = 40, p-value = 0.8031\n",
       "\n",
       "\n",
       "[[1]][[1]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y1 and y2\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 0.0072128, df = 1, p-value = 0.9323\n",
       "\n",
       "\n",
       "\n",
       "[[1]][[2]]\n",
       "[[1]][[2]]$Granger\n",
       "\n",
       "\tGranger causality H0: y2 do not Granger-cause y1\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 4.9053, df1 = 2, df2 = 40, p-value = 0.01244\n",
       "\n",
       "\n",
       "[[1]][[2]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y2 and y1\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 0.0072128, df = 1, p-value = 0.9323\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "[[2]]\n",
       "[[2]][[1]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y1\n",
       "                 y2\n",
       " [1,] -0.0048491853\n",
       " [2,] -0.0059185073\n",
       " [3,]  0.0315454235\n",
       " [4,]  0.0087346872\n",
       " [5,] -0.0102136254\n",
       " [6,] -0.0074972984\n",
       " [7,]  0.0008359199\n",
       " [8,]  0.0035575404\n",
       " [9,]  0.0012641122\n",
       "[10,] -0.0009572949\n",
       "[11,] -0.0009729775\n",
       "\n",
       "\n",
       "[[2]][[2]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y2\n",
       "                 y1\n",
       " [1,]  0.0000000000\n",
       " [2,] -0.0441398354\n",
       " [3,] -0.0286590787\n",
       " [4,]  0.0119928874\n",
       " [5,]  0.0168579529\n",
       " [6,]  0.0021561293\n",
       " [7,] -0.0061026227\n",
       " [8,] -0.0036817979\n",
       " [9,]  0.0009469562\n",
       "[10,]  0.0020057898\n",
       "[11,]  0.0004970752\n",
       "\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[[1]]\n",
       "[[1]][[1]]\n",
       "[[1]][[1]]$Granger\n",
       "\n",
       "\tGranger causality H0: y1 do not Granger-cause y2\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 0.81602, df1 = 2, df2 = 40, p-value = 0.4494\n",
       "\n",
       "\n",
       "[[1]][[1]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y1 and y2\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 0.065022, df = 1, p-value = 0.7987\n",
       "\n",
       "\n",
       "\n",
       "[[1]][[2]]\n",
       "[[1]][[2]]$Granger\n",
       "\n",
       "\tGranger causality H0: y2 do not Granger-cause y1\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 0.13476, df1 = 2, df2 = 40, p-value = 0.8743\n",
       "\n",
       "\n",
       "[[1]][[2]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y2 and y1\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 0.065022, df = 1, p-value = 0.7987\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "[[2]]\n",
       "[[2]][[1]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y1\n",
       "                 y2\n",
       " [1,]  0.0120371978\n",
       " [2,] -0.0432871492\n",
       " [3,] -0.0124419455\n",
       " [4,]  0.0368542994\n",
       " [5,] -0.0119905297\n",
       " [6,] -0.0001735252\n",
       " [7,]  0.0010610820\n",
       " [8,] -0.0074696293\n",
       " [9,]  0.0087669848\n",
       "[10,] -0.0022224948\n",
       "[11,] -0.0023870678\n",
       "\n",
       "\n",
       "[[2]][[2]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y2\n",
       "                 y1\n",
       " [1,]  0.0000000000\n",
       " [2,]  0.0067383716\n",
       " [3,] -0.0038387341\n",
       " [4,] -0.0015660218\n",
       " [5,]  0.0019654450\n",
       " [6,] -0.0012553818\n",
       " [7,]  0.0011891386\n",
       " [8,] -0.0002421695\n",
       " [9,] -0.0008084551\n",
       "[10,]  0.0008283136\n",
       "[11,] -0.0003728871\n",
       "\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[[1]]\n",
       "[[1]][[1]]\n",
       "[[1]][[1]]$Granger\n",
       "\n",
       "\tGranger causality H0: y1 do not Granger-cause y2\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 6.603, df1 = 2, df2 = 40, p-value = 0.003327\n",
       "\n",
       "\n",
       "[[1]][[1]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y1 and y2\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 3.5786, df = 1, p-value = 0.05853\n",
       "\n",
       "\n",
       "\n",
       "[[1]][[2]]\n",
       "[[1]][[2]]$Granger\n",
       "\n",
       "\tGranger causality H0: y2 do not Granger-cause y1\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 2.6733, df1 = 2, df2 = 40, p-value = 0.08134\n",
       "\n",
       "\n",
       "[[1]][[2]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y2 and y1\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 3.5786, df = 1, p-value = 0.05853\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "[[2]]\n",
       "[[2]][[1]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y1\n",
       "                 y2\n",
       " [1,] -3.436351e-03\n",
       " [2,] -4.883403e-03\n",
       " [3,] -4.619349e-03\n",
       " [4,] -2.880491e-03\n",
       " [5,] -1.415285e-03\n",
       " [6,] -4.612444e-04\n",
       " [7,] -8.343113e-06\n",
       " [8,]  1.393631e-04\n",
       " [9,]  1.388870e-04\n",
       "[10,]  9.215656e-05\n",
       "[11,]  4.631360e-05\n",
       "\n",
       "\n",
       "[[2]][[2]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y2\n",
       "                 y1\n",
       " [1,]  0.0000000000\n",
       " [2,]  0.0192658739\n",
       " [3,]  0.0126107842\n",
       " [4,]  0.0081368548\n",
       " [5,]  0.0034008973\n",
       " [6,]  0.0009335414\n",
       " [7,] -0.0002169094\n",
       " [8,] -0.0004919044\n",
       " [9,] -0.0004187496\n",
       "[10,] -0.0002531690\n",
       "[11,] -0.0001160284\n",
       "\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[[1]]\n",
       "[[1]][[1]]\n",
       "[[1]][[1]]$Granger\n",
       "\n",
       "\tGranger causality H0: y1 do not Granger-cause y2\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 19.141, df1 = 2, df2 = 40, p-value = 1.472e-06\n",
       "\n",
       "\n",
       "[[1]][[1]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y1 and y2\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 0.0037974, df = 1, p-value = 0.9509\n",
       "\n",
       "\n",
       "\n",
       "[[1]][[2]]\n",
       "[[1]][[2]]$Granger\n",
       "\n",
       "\tGranger causality H0: y2 do not Granger-cause y1\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 0.88834, df1 = 2, df2 = 40, p-value = 0.4193\n",
       "\n",
       "\n",
       "[[1]][[2]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y2 and y1\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 0.0037974, df = 1, p-value = 0.9509\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "[[2]]\n",
       "[[2]][[1]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y1\n",
       "                 y2\n",
       " [1,]  0.0006566363\n",
       " [2,]  0.0715030941\n",
       " [3,]  0.0487583250\n",
       " [4,]  0.0102634485\n",
       " [5,] -0.0043352292\n",
       " [6,] -0.0064779944\n",
       " [7,] -0.0058755823\n",
       " [8,] -0.0040515712\n",
       " [9,] -0.0018230108\n",
       "[10,] -0.0002302849\n",
       "[11,]  0.0004732102\n",
       "\n",
       "\n",
       "[[2]][[2]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y2\n",
       "                 y1\n",
       " [1,]  0.000000e+00\n",
       " [2,]  8.200878e-03\n",
       " [3,]  7.835571e-03\n",
       " [4,]  3.596718e-03\n",
       " [5,]  7.915618e-04\n",
       " [6,] -3.607974e-04\n",
       " [7,] -7.221566e-04\n",
       " [8,] -6.673396e-04\n",
       " [9,] -4.168480e-04\n",
       "[10,] -1.666428e-04\n",
       "[11,] -9.171496e-06\n",
       "\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[[1]]\n",
       "[[1]][[1]]\n",
       "[[1]][[1]]$Granger\n",
       "\n",
       "\tGranger causality H0: y1 do not Granger-cause y2\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 0.54756, df1 = 2, df2 = 40, p-value = 0.5826\n",
       "\n",
       "\n",
       "[[1]][[1]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y1 and y2\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 0.13517, df = 1, p-value = 0.7131\n",
       "\n",
       "\n",
       "\n",
       "[[1]][[2]]\n",
       "[[1]][[2]]$Granger\n",
       "\n",
       "\tGranger causality H0: y2 do not Granger-cause y1\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 7.8382, df1 = 2, df2 = 40, p-value = 0.001342\n",
       "\n",
       "\n",
       "[[1]][[2]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y2 and y1\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 0.13517, df = 1, p-value = 0.7131\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "[[2]]\n",
       "[[2]][[1]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y1\n",
       "                 y2\n",
       " [1,] -1.907485e-02\n",
       " [2,]  5.873935e-02\n",
       " [3,] -9.543727e-03\n",
       " [4,] -6.444629e-03\n",
       " [5,]  5.408614e-04\n",
       " [6,]  1.582902e-03\n",
       " [7,] -5.398579e-06\n",
       " [8,] -1.833858e-04\n",
       " [9,]  4.698496e-05\n",
       "[10,]  7.969486e-05\n",
       "[11,]  2.760679e-05\n",
       "\n",
       "\n",
       "[[2]][[2]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y2\n",
       "                y1\n",
       " [1,]  0.000000000\n",
       " [2,] -0.023115656\n",
       " [3,] -0.017968780\n",
       " [4,] -0.009559617\n",
       " [5,] -0.006923813\n",
       " [6,] -0.005916916\n",
       " [7,] -0.004533223\n",
       " [8,] -0.003294897\n",
       " [9,] -0.002464339\n",
       "[10,] -0.001877414\n",
       "[11,] -0.001418543\n",
       "\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[[1]]\n",
       "[[1]][[1]]\n",
       "[[1]][[1]]$Granger\n",
       "\n",
       "\tGranger causality H0: y1 do not Granger-cause y2\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 0.36644, df1 = 2, df2 = 40, p-value = 0.6955\n",
       "\n",
       "\n",
       "[[1]][[1]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y1 and y2\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 2.6481, df = 1, p-value = 0.1037\n",
       "\n",
       "\n",
       "\n",
       "[[1]][[2]]\n",
       "[[1]][[2]]$Granger\n",
       "\n",
       "\tGranger causality H0: y2 do not Granger-cause y1\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 0.01038, df1 = 2, df2 = 40, p-value = 0.9897\n",
       "\n",
       "\n",
       "[[1]][[2]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y2 and y1\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 2.6481, df = 1, p-value = 0.1037\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "[[2]]\n",
       "[[2]][[1]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y1\n",
       "                y2\n",
       " [1,] -0.097336235\n",
       " [2,] -0.008698907\n",
       " [3,]  0.038784461\n",
       " [4,]  0.026631719\n",
       " [5,]  0.019578834\n",
       " [6,]  0.012763426\n",
       " [7,]  0.008177499\n",
       " [8,]  0.005174288\n",
       " [9,]  0.003255560\n",
       "[10,]  0.002043684\n",
       "[11,]  0.001281386\n",
       "\n",
       "\n",
       "[[2]][[2]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y2\n",
       "                 y1\n",
       " [1,]  0.000000e+00\n",
       " [2,] -1.320523e-04\n",
       " [3,] -1.234045e-03\n",
       " [4,] -7.857208e-04\n",
       " [5,] -5.574709e-04\n",
       " [6,] -3.594645e-04\n",
       " [7,] -2.289556e-04\n",
       " [8,] -1.445307e-04\n",
       " [9,] -9.083167e-05\n",
       "[10,] -5.699009e-05\n",
       "[11,] -3.572410e-05\n",
       "\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[[1]]\n",
       "[[1]][[1]]\n",
       "[[1]][[1]]$Granger\n",
       "\n",
       "\tGranger causality H0: y1 do not Granger-cause y2\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 3.497, df1 = 2, df2 = 40, p-value = 0.03984\n",
       "\n",
       "\n",
       "[[1]][[1]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y1 and y2\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 1.7003, df = 1, p-value = 0.1922\n",
       "\n",
       "\n",
       "\n",
       "[[1]][[2]]\n",
       "[[1]][[2]]$Granger\n",
       "\n",
       "\tGranger causality H0: y2 do not Granger-cause y1\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 3.7178, df1 = 2, df2 = 40, p-value = 0.03304\n",
       "\n",
       "\n",
       "[[1]][[2]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y2 and y1\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 1.7003, df = 1, p-value = 0.1922\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "[[2]]\n",
       "[[2]][[1]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y1\n",
       "               y2\n",
       " [1,]  0.05692722\n",
       " [2,] -0.12937773\n",
       " [3,]  0.17072695\n",
       " [4,] -0.04025403\n",
       " [5,] -0.01558308\n",
       " [6,]  0.07943264\n",
       " [7,] -0.07062798\n",
       " [8,]  0.03278208\n",
       " [9,]  0.00946108\n",
       "[10,] -0.03653585\n",
       "[11,]  0.03560880\n",
       "\n",
       "\n",
       "[[2]][[2]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y2\n",
       "                 y1\n",
       " [1,]  0.0000000000\n",
       " [2,] -0.0136444807\n",
       " [3,] -0.0009508779\n",
       " [4,] -0.0021786468\n",
       " [5,] -0.0040218076\n",
       " [6,]  0.0032444002\n",
       " [7,] -0.0018527659\n",
       " [8,]  0.0001741941\n",
       " [9,]  0.0015748676\n",
       "[10,] -0.0016176405\n",
       "[11,]  0.0010864211\n",
       "\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[[1]]\n",
       "[[1]][[1]]\n",
       "[[1]][[1]]$Granger\n",
       "\n",
       "\tGranger causality H0: y1 do not Granger-cause y2\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 9.5503, df1 = 2, df2 = 40, p-value = 0.0004068\n",
       "\n",
       "\n",
       "[[1]][[1]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y1 and y2\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 4.4892, df = 1, p-value = 0.03411\n",
       "\n",
       "\n",
       "\n",
       "[[1]][[2]]\n",
       "[[1]][[2]]$Granger\n",
       "\n",
       "\tGranger causality H0: y2 do not Granger-cause y1\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 1.7433, df1 = 2, df2 = 40, p-value = 0.188\n",
       "\n",
       "\n",
       "[[1]][[2]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y2 and y1\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 4.4892, df = 1, p-value = 0.03411\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "[[2]]\n",
       "[[2]][[1]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y1\n",
       "                y2\n",
       " [1,] -0.030301622\n",
       " [2,] -0.061560380\n",
       " [3,]  0.020799773\n",
       " [4,]  0.035835506\n",
       " [5,]  0.027342575\n",
       " [6,]  0.022990724\n",
       " [7,]  0.006806457\n",
       " [8,] -0.008002935\n",
       " [9,] -0.013446002\n",
       "[10,] -0.012670379\n",
       "[11,] -0.007515900\n",
       "\n",
       "\n",
       "[[2]][[2]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y2\n",
       "                 y1\n",
       " [1,]  0.0000000000\n",
       " [2,] -0.0021881730\n",
       " [3,] -0.0033383389\n",
       " [4,] -0.0020321631\n",
       " [5,] -0.0006421774\n",
       " [6,]  0.0004735426\n",
       " [7,]  0.0011848241\n",
       " [8,]  0.0011899255\n",
       " [9,]  0.0007262350\n",
       "[10,]  0.0001478107\n",
       "[11,] -0.0003120188\n",
       "\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[[1]]\n",
       "[[1]][[1]]\n",
       "[[1]][[1]]$Granger\n",
       "\n",
       "\tGranger causality H0: y1 do not Granger-cause y2\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 0.0037757, df1 = 2, df2 = 40, p-value = 0.9962\n",
       "\n",
       "\n",
       "[[1]][[1]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y1 and y2\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 0.68696, df = 1, p-value = 0.4072\n",
       "\n",
       "\n",
       "\n",
       "[[1]][[2]]\n",
       "[[1]][[2]]$Granger\n",
       "\n",
       "\tGranger causality H0: y2 do not Granger-cause y1\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 4.6537, df1 = 2, df2 = 40, p-value = 0.01524\n",
       "\n",
       "\n",
       "[[1]][[2]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y2 and y1\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 0.68696, df = 1, p-value = 0.4072\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "[[2]]\n",
       "[[2]][[1]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y1\n",
       "                 y2\n",
       " [1,] -0.0446331304\n",
       " [2,] -0.0011793739\n",
       " [3,]  0.0040999918\n",
       " [4,] -0.0024040258\n",
       " [5,] -0.0026171945\n",
       " [6,] -0.0009093545\n",
       " [7,] -0.0004818500\n",
       " [8,] -0.0005132834\n",
       " [9,] -0.0003952204\n",
       "[10,] -0.0002506671\n",
       "[11,] -0.0001728649\n",
       "\n",
       "\n",
       "[[2]][[2]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y2\n",
       "                y1\n",
       " [1,] 0.0000000000\n",
       " [2,] 0.0043424960\n",
       " [3,] 0.0071086811\n",
       " [4,] 0.0040954722\n",
       " [5,] 0.0021994713\n",
       " [6,] 0.0017350860\n",
       " [7,] 0.0013629846\n",
       " [8,] 0.0009394510\n",
       " [9,] 0.0006452158\n",
       "[10,] 0.0004643939\n",
       "[11,] 0.0003345525\n",
       "\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[[1]]\n",
       "[[1]][[1]]\n",
       "[[1]][[1]]$Granger\n",
       "\n",
       "\tGranger causality H0: y1 do not Granger-cause y2\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 1.0529, df1 = 2, df2 = 40, p-value = 0.3584\n",
       "\n",
       "\n",
       "[[1]][[1]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y1 and y2\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 2.5449, df = 1, p-value = 0.1106\n",
       "\n",
       "\n",
       "\n",
       "[[1]][[2]]\n",
       "[[1]][[2]]$Granger\n",
       "\n",
       "\tGranger causality H0: y2 do not Granger-cause y1\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 1.33, df1 = 2, df2 = 40, p-value = 0.2759\n",
       "\n",
       "\n",
       "[[1]][[2]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y2 and y1\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 2.5449, df = 1, p-value = 0.1106\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "[[2]]\n",
       "[[2]][[1]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y1\n",
       "                y2\n",
       " [1,]  0.092206964\n",
       " [2,] -0.091606995\n",
       " [3,] -0.048588512\n",
       " [4,] -0.029674907\n",
       " [5,]  0.004294772\n",
       " [6,]  0.012731573\n",
       " [7,]  0.010744213\n",
       " [8,]  0.003535347\n",
       " [9,] -0.001259841\n",
       "[10,] -0.002681108\n",
       "[11,] -0.001796998\n",
       "\n",
       "\n",
       "[[2]][[2]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y2\n",
       "                 y1\n",
       " [1,]  0.000000e+00\n",
       " [2,]  1.941366e-04\n",
       " [3,]  3.614636e-03\n",
       " [4,]  2.253150e-03\n",
       " [5,]  8.200843e-04\n",
       " [6,] -3.674969e-04\n",
       " [7,] -6.210375e-04\n",
       " [8,] -4.091188e-04\n",
       " [9,] -8.920744e-05\n",
       "[10,]  9.258246e-05\n",
       "[11,]  1.203135e-04\n",
       "\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[[1]]\n",
       "[[1]][[1]]\n",
       "[[1]][[1]]$Granger\n",
       "\n",
       "\tGranger causality H0: y1 do not Granger-cause y2\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 1.23, df1 = 2, df2 = 40, p-value = 0.3031\n",
       "\n",
       "\n",
       "[[1]][[1]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y1 and y2\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 0.0045908, df = 1, p-value = 0.946\n",
       "\n",
       "\n",
       "\n",
       "[[1]][[2]]\n",
       "[[1]][[2]]$Granger\n",
       "\n",
       "\tGranger causality H0: y2 do not Granger-cause y1\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 0.39508, df1 = 2, df2 = 40, p-value = 0.6762\n",
       "\n",
       "\n",
       "[[1]][[2]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y2 and y1\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 0.0045908, df = 1, p-value = 0.946\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "[[2]]\n",
       "[[2]][[1]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y1\n",
       "                y2\n",
       " [1,]  0.003135304\n",
       " [2,] -0.082099442\n",
       " [3,]  0.017303506\n",
       " [4,] -0.004915302\n",
       " [5,] -0.022794047\n",
       " [6,]  0.028507410\n",
       " [7,] -0.017964180\n",
       " [8,]  0.002282935\n",
       " [9,]  0.011035512\n",
       "[10,] -0.015107835\n",
       "[11,]  0.010659662\n",
       "\n",
       "\n",
       "[[2]][[2]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y2\n",
       "                 y1\n",
       " [1,]  0.000000e+00\n",
       " [2,]  1.916817e-03\n",
       " [3,]  6.359499e-04\n",
       " [4,] -4.138973e-04\n",
       " [5,]  7.600240e-04\n",
       " [6,] -4.573573e-04\n",
       " [7,]  2.745819e-06\n",
       " [8,]  2.978184e-04\n",
       " [9,] -3.959660e-04\n",
       "[10,]  2.603320e-04\n",
       "[11,] -3.410048e-05\n",
       "\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[[1]]\n",
       "[[1]][[1]]\n",
       "[[1]][[1]]$Granger\n",
       "\n",
       "\tGranger causality H0: y1 do not Granger-cause y2\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 0.58518, df1 = 2, df2 = 40, p-value = 0.5617\n",
       "\n",
       "\n",
       "[[1]][[1]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y1 and y2\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 0.040087, df = 1, p-value = 0.8413\n",
       "\n",
       "\n",
       "\n",
       "[[1]][[2]]\n",
       "[[1]][[2]]$Granger\n",
       "\n",
       "\tGranger causality H0: y2 do not Granger-cause y1\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 11.063, df1 = 2, df2 = 40, p-value = 0.0001499\n",
       "\n",
       "\n",
       "[[1]][[2]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y2 and y1\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 0.040087, df = 1, p-value = 0.8413\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "[[2]]\n",
       "[[2]][[1]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y1\n",
       "                 y2\n",
       " [1,] -1.035021e-02\n",
       " [2,] -3.464042e-02\n",
       " [3,]  1.882703e-02\n",
       " [4,]  2.467958e-02\n",
       " [5,]  2.855129e-04\n",
       " [6,]  7.330690e-04\n",
       " [7,]  3.233094e-03\n",
       " [8,] -2.870152e-03\n",
       " [9,] -3.128941e-03\n",
       "[10,] -1.533883e-05\n",
       "[11,]  1.367073e-05\n",
       "\n",
       "\n",
       "[[2]][[2]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y2\n",
       "                 y1\n",
       " [1,]  0.0000000000\n",
       " [2,] -0.0003990350\n",
       " [3,] -0.0638406995\n",
       " [4,] -0.0405678119\n",
       " [5,]  0.0023388751\n",
       " [6,]  0.0028518889\n",
       " [7,]  0.0022699268\n",
       " [8,]  0.0084934523\n",
       " [9,]  0.0047164793\n",
       "[10,] -0.0007061074\n",
       "[11,] -0.0007500051\n",
       "\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[[1]]\n",
       "[[1]][[1]]\n",
       "[[1]][[1]]$Granger\n",
       "\n",
       "\tGranger causality H0: y1 do not Granger-cause y2\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 4.2859, df1 = 2, df2 = 40, p-value = 0.02058\n",
       "\n",
       "\n",
       "[[1]][[1]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y1 and y2\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 1.6725, df = 1, p-value = 0.1959\n",
       "\n",
       "\n",
       "\n",
       "[[1]][[2]]\n",
       "[[1]][[2]]$Granger\n",
       "\n",
       "\tGranger causality H0: y2 do not Granger-cause y1\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 1.554, df1 = 2, df2 = 40, p-value = 0.2239\n",
       "\n",
       "\n",
       "[[1]][[2]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y2 and y1\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 1.6725, df = 1, p-value = 0.1959\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "[[2]]\n",
       "[[2]][[1]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y1\n",
       "                 y2\n",
       " [1,] -0.0645597949\n",
       " [2,] -0.0272612070\n",
       " [3,]  0.1227786054\n",
       " [4,]  0.0669878166\n",
       " [5,]  0.0314894809\n",
       " [6,] -0.0193043135\n",
       " [7,] -0.0199559070\n",
       " [8,] -0.0133848302\n",
       " [9,]  0.0006943244\n",
       "[10,]  0.0045755677\n",
       "[11,]  0.0044483816\n",
       "\n",
       "\n",
       "[[2]][[2]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y2\n",
       "                 y1\n",
       " [1,]  0.000000e+00\n",
       " [2,] -2.808926e-02\n",
       " [3,] -6.887717e-03\n",
       " [4,] -2.753397e-03\n",
       " [5,]  5.971615e-03\n",
       " [6,]  3.064552e-03\n",
       " [7,]  1.659313e-03\n",
       " [8,] -9.437486e-04\n",
       " [9,] -9.241639e-04\n",
       "[10,] -6.719455e-04\n",
       "[11,]  3.141335e-05\n",
       "\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[[1]]\n",
       "[[1]][[1]]\n",
       "[[1]][[1]]$Granger\n",
       "\n",
       "\tGranger causality H0: y1 do not Granger-cause y2\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 4.1557, df1 = 2, df2 = 40, p-value = 0.02292\n",
       "\n",
       "\n",
       "[[1]][[1]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y1 and y2\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 0.14797, df = 1, p-value = 0.7005\n",
       "\n",
       "\n",
       "\n",
       "[[1]][[2]]\n",
       "[[1]][[2]]$Granger\n",
       "\n",
       "\tGranger causality H0: y2 do not Granger-cause y1\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 1.2625, df1 = 2, df2 = 40, p-value = 0.294\n",
       "\n",
       "\n",
       "[[1]][[2]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y2 and y1\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 0.14797, df = 1, p-value = 0.7005\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "[[2]]\n",
       "[[2]][[1]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y1\n",
       "                y2\n",
       " [1,] -0.015897912\n",
       " [2,]  0.147432315\n",
       " [3,] -0.144641097\n",
       " [4,]  0.046265389\n",
       " [5,]  0.017016557\n",
       " [6,] -0.059797930\n",
       " [7,]  0.063450346\n",
       " [8,] -0.031038984\n",
       " [9,] -0.003656091\n",
       "[10,]  0.025321805\n",
       "[11,] -0.028745615\n",
       "\n",
       "\n",
       "[[2]][[2]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y2\n",
       "                 y1\n",
       " [1,]  0.000000e+00\n",
       " [2,]  1.098137e-02\n",
       " [3,] -1.271698e-02\n",
       " [4,]  4.557000e-03\n",
       " [5,]  8.090224e-04\n",
       " [6,] -4.548403e-03\n",
       " [7,]  5.367884e-03\n",
       " [8,] -2.910650e-03\n",
       " [9,]  1.318016e-05\n",
       "[10,]  1.914223e-03\n",
       "[11,] -2.392381e-03\n",
       "\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[[1]]\n",
       "[[1]][[1]]\n",
       "[[1]][[1]]$Granger\n",
       "\n",
       "\tGranger causality H0: y1 do not Granger-cause y2\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 0.63316, df1 = 2, df2 = 40, p-value = 0.5361\n",
       "\n",
       "\n",
       "[[1]][[1]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y1 and y2\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 0.59555, df = 1, p-value = 0.4403\n",
       "\n",
       "\n",
       "\n",
       "[[1]][[2]]\n",
       "[[1]][[2]]$Granger\n",
       "\n",
       "\tGranger causality H0: y2 do not Granger-cause y1\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 1.0202, df1 = 2, df2 = 40, p-value = 0.3697\n",
       "\n",
       "\n",
       "[[1]][[2]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y2 and y1\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 0.59555, df = 1, p-value = 0.4403\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "[[2]]\n",
       "[[2]][[1]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y1\n",
       "                 y2\n",
       " [1,]  0.0436973131\n",
       " [2,]  0.0494480612\n",
       " [3,]  0.0350570124\n",
       " [4,] -0.0150019398\n",
       " [5,] -0.0062602010\n",
       " [6,]  0.0064019215\n",
       " [7,]  0.0005124686\n",
       " [8,] -0.0021392317\n",
       " [9,]  0.0003385100\n",
       "[10,]  0.0005972814\n",
       "[11,] -0.0002465641\n",
       "\n",
       "\n",
       "[[2]][[2]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y2\n",
       "                 y1\n",
       " [1,]  0.0000000000\n",
       " [2,]  0.0765903961\n",
       " [3,]  0.0260430988\n",
       " [4,] -0.0238644742\n",
       " [5,] -0.0016963421\n",
       " [6,]  0.0080840360\n",
       " [7,] -0.0013313112\n",
       " [8,] -0.0022413601\n",
       " [9,]  0.0009450773\n",
       "[10,]  0.0004878071\n",
       "[11,] -0.0004127437\n",
       "\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[[1]]\n",
       "[[1]][[1]]\n",
       "[[1]][[1]]$Granger\n",
       "\n",
       "\tGranger causality H0: y1 do not Granger-cause y2\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 1.6971, df1 = 2, df2 = 40, p-value = 0.1961\n",
       "\n",
       "\n",
       "[[1]][[1]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y1 and y2\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 0.15945, df = 1, p-value = 0.6897\n",
       "\n",
       "\n",
       "\n",
       "[[1]][[2]]\n",
       "[[1]][[2]]$Granger\n",
       "\n",
       "\tGranger causality H0: y2 do not Granger-cause y1\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 2.2174, df1 = 2, df2 = 40, p-value = 0.1221\n",
       "\n",
       "\n",
       "[[1]][[2]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y2 and y1\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 0.15945, df = 1, p-value = 0.6897\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "[[2]]\n",
       "[[2]][[1]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y1\n",
       "                y2\n",
       " [1,] -0.018159119\n",
       " [2,]  0.045711993\n",
       " [3,]  0.040821109\n",
       " [4,] -0.073999227\n",
       " [5,]  0.035983029\n",
       " [6,] -0.003276566\n",
       " [7,] -0.013012096\n",
       " [8,]  0.024414522\n",
       " [9,] -0.023673095\n",
       "[10,]  0.010325598\n",
       "[11,]  0.003860160\n",
       "\n",
       "\n",
       "[[2]][[2]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y2\n",
       "                 y1\n",
       " [1,]  0.0000000000\n",
       " [2,] -0.0687494005\n",
       " [3,] -0.0208199587\n",
       " [4,]  0.0769429415\n",
       " [5,] -0.0432415674\n",
       " [6,]  0.0083680943\n",
       " [7,]  0.0073442407\n",
       " [8,] -0.0210247875\n",
       " [9,]  0.0249378423\n",
       "[10,] -0.0139378422\n",
       "[11,] -0.0006391833\n",
       "\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[[1]]\n",
       "[[1]][[1]]\n",
       "[[1]][[1]]$Granger\n",
       "\n",
       "\tGranger causality H0: y1 do not Granger-cause y2\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 0.033704, df1 = 2, df2 = 40, p-value = 0.9669\n",
       "\n",
       "\n",
       "[[1]][[1]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y1 and y2\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 0.24038, df = 1, p-value = 0.6239\n",
       "\n",
       "\n",
       "\n",
       "[[1]][[2]]\n",
       "[[1]][[2]]$Granger\n",
       "\n",
       "\tGranger causality H0: y2 do not Granger-cause y1\n",
       "\n",
       "data:  VAR object model\n",
       "F-Test = 2.4985, df1 = 2, df2 = 40, p-value = 0.09496\n",
       "\n",
       "\n",
       "[[1]][[2]]$Instant\n",
       "\n",
       "\tH0: No instantaneous causality between: y2 and y1\n",
       "\n",
       "data:  VAR object model\n",
       "Chi-squared = 0.24038, df = 1, p-value = 0.6239\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "[[2]]\n",
       "[[2]][[1]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y1\n",
       "                 y2\n",
       " [1,] -2.411141e-02\n",
       " [2,]  2.379493e-02\n",
       " [3,]  1.662515e-03\n",
       " [4,] -1.498251e-02\n",
       " [5,]  1.584177e-02\n",
       " [6,] -8.669036e-03\n",
       " [7,] -3.410830e-04\n",
       " [8,]  6.040227e-03\n",
       " [9,] -6.728445e-03\n",
       "[10,]  3.814677e-03\n",
       "[11,] -1.340125e-05\n",
       "\n",
       "\n",
       "[[2]][[2]]\n",
       "\n",
       "Impulse response coefficients\n",
       "$y2\n",
       "                y1\n",
       " [1,]  0.000000000\n",
       " [2,]  0.098766309\n",
       " [3,] -0.080180938\n",
       " [4,]  0.028235958\n",
       " [5,]  0.017897705\n",
       " [6,] -0.039271788\n",
       " [7,]  0.033603223\n",
       " [8,] -0.013104072\n",
       " [9,] -0.006777444\n",
       "[10,]  0.016325095\n",
       "[11,] -0.014381143\n",
       "\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Y1～11\n",
    "ts(relation$Y1, relation$Y2)\n",
    "ts(relation$Y1, relation$Y3)\n",
    "ts(relation$Y1, relation$Y4)\n",
    "ts(relation$Y1, relation$Y5)\n",
    "ts(relation$Y1, relation$Y6)\n",
    "ts(relation$Y1, relation$Y7)\n",
    "ts(relation$Y1, relation$Y8)\n",
    "ts(relation$Y1, relation$Y9)\n",
    "ts(relation$Y1, relation$Y10)\n",
    "ts(relation$Y1, relation$Y11)\n",
    "#Y2～11\n",
    "ts(relation$Y2, relation$Y3)\n",
    "ts(relation$Y2, relation$Y4)\n",
    "ts(relation$Y2, relation$Y5)\n",
    "ts(relation$Y2, relation$Y6)\n",
    "ts(relation$Y2, relation$Y7)\n",
    "ts(relation$Y2, relation$Y8)\n",
    "ts(relation$Y2, relation$Y9)\n",
    "ts(relation$Y2, relation$Y10)\n",
    "ts(relation$Y2, relation$Y11)\n",
    "#Y3～11\n",
    "ts(relation$Y3, relation$Y4)\n",
    "ts(relation$Y3, relation$Y5)\n",
    "ts(relation$Y3, relation$Y6)\n",
    "ts(relation$Y3, relation$Y7)\n",
    "ts(relation$Y3, relation$Y8)\n",
    "ts(relation$Y3, relation$Y9)\n",
    "ts(relation$Y3, relation$Y10)\n",
    "ts(relation$Y3, relation$Y11)\n",
    "#Y4～11\n",
    "ts(relation$Y4, relation$Y5)\n",
    "ts(relation$Y4, relation$Y6)\n",
    "ts(relation$Y4, relation$Y7)\n",
    "ts(relation$Y4, relation$Y8)\n",
    "ts(relation$Y4, relation$Y9)\n",
    "ts(relation$Y4, relation$Y10)\n",
    "ts(relation$Y4, relation$Y11)\n",
    "#Y5～11\n",
    "ts(relation$Y5, relation$Y6)\n",
    "ts(relation$Y5, relation$Y7)\n",
    "ts(relation$Y5, relation$Y8)\n",
    "ts(relation$Y5, relation$Y9)\n",
    "ts(relation$Y5, relation$Y10)\n",
    "ts(relation$Y5, relation$Y11)\n",
    "#Y6～11\n",
    "ts(relation$Y6, relation$Y7)\n",
    "ts(relation$Y6, relation$Y8)\n",
    "ts(relation$Y6, relation$Y9)\n",
    "ts(relation$Y6, relation$Y10)\n",
    "ts(relation$Y6, relation$Y11)\n",
    "#Y7～11\n",
    "ts(relation$Y7, relation$Y8)\n",
    "ts(relation$Y7, relation$Y9)\n",
    "ts(relation$Y7, relation$Y10)\n",
    "ts(relation$Y7, relation$Y11)\n",
    "#Y8～11\n",
    "ts(relation$Y8, relation$Y9)\n",
    "ts(relation$Y8, relation$Y10)\n",
    "ts(relation$Y8, relation$Y11)\n",
    "#Y9～11\n",
    "ts(relation$Y9, relation$Y10)\n",
    "ts(relation$Y9, relation$Y11)\n",
    "#Y10～11\n",
    "ts(relation$Y10, relation$Y11)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## パネルVARモデルによる動的直接相関係数の導出\n",
    "1. パネルデータの生成\n",
    "1. 目視確認\n",
    "1. 要約統計量を求める。\n",
    "1. モデル形成\n",
    "1. 詳細結果の目視確認\n",
    "1. 動的直接相関係数の抽出\n",
    "1. パネルVARモデルの標準誤差の抽出"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A pdata.frame: 28 × 13</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>id</th><th scope=col>time</th><th scope=col>Y1</th><th scope=col>Y2</th><th scope=col>Y3</th><th scope=col>Y4</th><th scope=col>Y5</th><th scope=col>Y6</th><th scope=col>Y7</th><th scope=col>Y8</th><th scope=col>Y9</th><th scope=col>Y10</th><th scope=col>Y11</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>0-1987</th><td>0</td><td>1987</td><td> 0.046607215</td><td>-0.0002078956</td><td> 0.0</td><td> 0.14329221</td><td> 0.1707078336</td><td>-0.011862535</td><td> 0.000000000</td><td> 0.07410797</td><td> 0.142432215</td><td> 0.0000000000</td><td> 0.0</td></tr>\n",
       "\t<tr><th scope=row>0-1988</th><td>0</td><td>1988</td><td> 0.054685886</td><td> 0.0659408662</td><td> 0.0</td><td>-0.27232247</td><td> 0.1875246945</td><td>-0.036456042</td><td> 0.006960585</td><td> 0.00000000</td><td> 0.328497539</td><td> 0.0000000000</td><td> 0.0</td></tr>\n",
       "\t<tr><th scope=row>0-1989</th><td>0</td><td>1989</td><td> 0.034524371</td><td> 0.0250528266</td><td> 0.0</td><td> 0.19834206</td><td> 0.0005499123</td><td>-0.042990185</td><td> 0.022858138</td><td>-0.11332869</td><td> 0.232060601</td><td> 0.0000000000</td><td> 0.0</td></tr>\n",
       "\t<tr><th scope=row>0-1990</th><td>0</td><td>1990</td><td> 0.038998671</td><td> 0.0696834290</td><td> 0.0</td><td> 0.21128783</td><td> 0.0213534086</td><td>-0.050341755</td><td> 0.030052345</td><td>-0.08338161</td><td>-0.519724355</td><td>-0.0870144800</td><td> 0.0</td></tr>\n",
       "\t<tr><th scope=row>0-1991</th><td>0</td><td>1991</td><td> 0.014394372</td><td> 0.0108691005</td><td>-0.5</td><td>-0.15698568</td><td> 0.1077704117</td><td>-0.016438726</td><td> 0.033426293</td><td>-0.09097178</td><td>-0.070368088</td><td> 0.0000000000</td><td> 0.0</td></tr>\n",
       "\t<tr><th scope=row>0-1992</th><td>0</td><td>1992</td><td> 0.008714600</td><td> 0.0088384495</td><td> 0.5</td><td>-0.03704127</td><td> 0.0555237346</td><td> 0.019152432</td><td> 0.015781495</td><td> 0.00000000</td><td>-0.321780974</td><td>-0.2076436613</td><td>-0.5</td></tr>\n",
       "\t<tr><th scope=row>0-1993</th><td>0</td><td>1993</td><td> 0.010772713</td><td> 0.0018245193</td><td> 0.0</td><td>-0.15539469</td><td> 0.1027742980</td><td> 0.078164773</td><td> 0.013478690</td><td> 0.04652002</td><td> 0.015192971</td><td> 0.0000000000</td><td> 0.5</td></tr>\n",
       "\t<tr><th scope=row>0-1994</th><td>0</td><td>1994</td><td> 0.034959773</td><td> 0.0388160427</td><td> 0.0</td><td>-0.02806837</td><td> 0.0898896168</td><td> 0.029631798</td><td> 0.006160184</td><td> 0.12783337</td><td> 0.118167799</td><td> 0.0000000000</td><td>-0.5</td></tr>\n",
       "\t<tr><th scope=row>0-1995</th><td>0</td><td>1995</td><td> 0.031159245</td><td> 0.0255089921</td><td>-0.5</td><td>-0.01487016</td><td> 0.0913040437</td><td> 0.059049029</td><td>-0.001024066</td><td> 0.14842001</td><td> 0.008353525</td><td> 0.0000000000</td><td> 0.5</td></tr>\n",
       "\t<tr><th scope=row>0-1996</th><td>0</td><td>1996</td><td> 0.015857216</td><td> 0.0017373256</td><td> 0.5</td><td> 0.19217123</td><td>-0.1259988250</td><td> 0.060084811</td><td> 0.001024066</td><td> 0.09844007</td><td>-0.026863215</td><td> 0.0868586003</td><td> 0.0</td></tr>\n",
       "\t<tr><th scope=row>0-1997</th><td>0</td><td>1997</td><td> 0.007429288</td><td>-0.0068458719</td><td> 0.0</td><td>-0.01512484</td><td>-0.0896433402</td><td> 0.044357853</td><td> 0.018256085</td><td> 0.06062462</td><td>-0.256382432</td><td> 0.0000000000</td><td>-0.5</td></tr>\n",
       "\t<tr><th scope=row>0-1998</th><td>0</td><td>1998</td><td>-0.013352105</td><td>-0.0308981561</td><td> 0.0</td><td>-0.33871590</td><td>-0.0915822665</td><td> 0.147635999</td><td> 0.006012042</td><td> 0.00000000</td><td>-0.103444736</td><td> 0.0000000000</td><td> 0.5</td></tr>\n",
       "\t<tr><th scope=row>0-1999</th><td>0</td><td>1999</td><td> 0.024479115</td><td> 0.0199589931</td><td>-0.5</td><td> 0.34363431</td><td> 0.1051332959</td><td> 0.123904093</td><td>-0.003001503</td><td> 0.18721154</td><td> 0.316258976</td><td> 0.0000000000</td><td> 0.0</td></tr>\n",
       "\t<tr><th scope=row>0-2000</th><td>0</td><td>2000</td><td> 0.010736315</td><td> 0.0051233463</td><td> 0.5</td><td> 0.45901574</td><td> 0.0628943640</td><td> 0.090286847</td><td>-0.007038742</td><td> 0.13657554</td><td>-0.310307387</td><td> 0.0195966413</td><td> 0.0</td></tr>\n",
       "\t<tr><th scope=row>0-2001</th><td>0</td><td>2001</td><td>-0.012860210</td><td>-0.0130581957</td><td>-0.5</td><td>-0.10950287</td><td>-0.1242445967</td><td> 0.084129531</td><td>-0.007088637</td><td> 0.00000000</td><td>-0.261116345</td><td> 0.0000000000</td><td> 0.0</td></tr>\n",
       "\t<tr><th scope=row>0-2002</th><td>0</td><td>2002</td><td> 0.014477534</td><td> 0.0245535667</td><td> 0.5</td><td> 0.03522143</td><td>-0.0377329263</td><td> 0.078820960</td><td>-0.009188426</td><td> 0.06187540</td><td>-0.196926170</td><td> 0.0000000000</td><td> 0.0</td></tr>\n",
       "\t<tr><th scope=row>0-2004</th><td>0</td><td>2004</td><td> 0.013203359</td><td>-0.0004923830</td><td>-0.5</td><td> 0.20097118</td><td> 0.0890274168</td><td> 0.082655722</td><td> 0.000000000</td><td>-0.01869213</td><td> 0.073310952</td><td> 0.0000000000</td><td> 0.0</td></tr>\n",
       "\t<tr><th scope=row>0-2005</th><td>0</td><td>2005</td><td>-0.004302008</td><td> 0.0087232322</td><td> 0.5</td><td> 0.31611580</td><td>-0.0094022598</td><td> 0.044905504</td><td>-0.002059733</td><td>-0.12014431</td><td> 0.341250994</td><td> 0.2223047186</td><td>-0.5</td></tr>\n",
       "\t<tr><th scope=row>0-2006</th><td>0</td><td>2006</td><td> 0.002756154</td><td>-0.0097746472</td><td> 0.0</td><td> 0.16065287</td><td>-0.0484778223</td><td> 0.002989539</td><td> 0.002059733</td><td>-0.06595797</td><td> 0.063789817</td><td> 0.0000000000</td><td> 0.5</td></tr>\n",
       "\t<tr><th scope=row>0-2008</th><td>0</td><td>2008</td><td>-0.072976198</td><td>-0.0535455057</td><td> 0.5</td><td> 0.23562571</td><td> 0.1064363396</td><td> 0.049723435</td><td> 0.014300550</td><td>-0.05001042</td><td>-0.561164650</td><td> 0.0000000000</td><td>-0.5</td></tr>\n",
       "\t<tr><th scope=row>0-2009</th><td>0</td><td>2009</td><td>-0.018964601</td><td>-0.0709879183</td><td> 0.0</td><td>-0.38749772</td><td> 0.0376843968</td><td> 0.118611879</td><td>-0.014300550</td><td> 0.02531781</td><td> 0.188591815</td><td>-0.9112279213</td><td> 0.5</td></tr>\n",
       "\t<tr><th scope=row>0-2011</th><td>0</td><td>2011</td><td>-0.027442840</td><td> 0.0337387012</td><td> 0.0</td><td> 0.17035756</td><td> 0.0914818714</td><td> 0.061593011</td><td>-0.002074690</td><td> 0.00000000</td><td>-0.188344934</td><td> 0.0000000000</td><td> 0.5</td></tr>\n",
       "\t<tr><th scope=row>0-2012</th><td>0</td><td>2012</td><td>-0.012998704</td><td> 0.0571105641</td><td> 0.0</td><td> 0.01671820</td><td> 0.0146306523</td><td> 0.049632624</td><td>-0.001038961</td><td>-0.10318424</td><td> 0.207581791</td><td> 0.9044428289</td><td>-0.5</td></tr>\n",
       "\t<tr><th scope=row>0-2013</th><td>0</td><td>2013</td><td>-0.007562834</td><td>-0.1167735606</td><td> 0.0</td><td>-0.00462769</td><td>-0.1828863972</td><td> 0.028365790</td><td> 0.004149384</td><td>-0.06744128</td><td> 0.445140246</td><td> 0.0000000000</td><td> 0.0</td></tr>\n",
       "\t<tr><th scope=row>1-2003</th><td>1</td><td>2003</td><td>-0.009538650</td><td> 0.0039634439</td><td> 0.0</td><td> 0.09713002</td><td> 0.0843496712</td><td> 0.075329719</td><td>-0.003081667</td><td> 0.07696104</td><td> 0.221828367</td><td> 0.0170148206</td><td> 0.0</td></tr>\n",
       "\t<tr><th scope=row>1-2007</th><td>1</td><td>2007</td><td>-0.017173710</td><td> 0.0245622798</td><td>-0.5</td><td> 0.04150997</td><td>-0.0002505979</td><td> 0.014815086</td><td> 0.000000000</td><td>-0.07061757</td><td>-0.118048778</td><td> 0.0000000000</td><td> 0.0</td></tr>\n",
       "\t<tr><th scope=row>1-2010</th><td>1</td><td>2010</td><td> 0.042285908</td><td> 0.0455902315</td><td> 0.0</td><td> 0.21075852</td><td> 0.0715071253</td><td> 0.054808236</td><td>-0.007227703</td><td> 0.24294618</td><td>-0.023341674</td><td> 0.0000000000</td><td>-0.5</td></tr>\n",
       "\t<tr><th scope=row>1-2014</th><td>1</td><td>2014</td><td>-0.032698672</td><td>-0.0364950713</td><td> 0.0</td><td>-0.06275503</td><td>-0.0603498861</td><td> 0.019588603</td><td> 0.026559273</td><td>-0.07232066</td><td> 0.042192672</td><td> 0.0002122224</td><td> 0.5</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A pdata.frame: 28 × 13\n",
       "\\begin{tabular}{r|lllllllllllll}\n",
       "  & id & time & Y1 & Y2 & Y3 & Y4 & Y5 & Y6 & Y7 & Y8 & Y9 & Y10 & Y11\\\\\n",
       "  & <fct> & <fct> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t0-1987 & 0 & 1987 &  0.046607215 & -0.0002078956 &  0.0 &  0.14329221 &  0.1707078336 & -0.011862535 &  0.000000000 &  0.07410797 &  0.142432215 &  0.0000000000 &  0.0\\\\\n",
       "\t0-1988 & 0 & 1988 &  0.054685886 &  0.0659408662 &  0.0 & -0.27232247 &  0.1875246945 & -0.036456042 &  0.006960585 &  0.00000000 &  0.328497539 &  0.0000000000 &  0.0\\\\\n",
       "\t0-1989 & 0 & 1989 &  0.034524371 &  0.0250528266 &  0.0 &  0.19834206 &  0.0005499123 & -0.042990185 &  0.022858138 & -0.11332869 &  0.232060601 &  0.0000000000 &  0.0\\\\\n",
       "\t0-1990 & 0 & 1990 &  0.038998671 &  0.0696834290 &  0.0 &  0.21128783 &  0.0213534086 & -0.050341755 &  0.030052345 & -0.08338161 & -0.519724355 & -0.0870144800 &  0.0\\\\\n",
       "\t0-1991 & 0 & 1991 &  0.014394372 &  0.0108691005 & -0.5 & -0.15698568 &  0.1077704117 & -0.016438726 &  0.033426293 & -0.09097178 & -0.070368088 &  0.0000000000 &  0.0\\\\\n",
       "\t0-1992 & 0 & 1992 &  0.008714600 &  0.0088384495 &  0.5 & -0.03704127 &  0.0555237346 &  0.019152432 &  0.015781495 &  0.00000000 & -0.321780974 & -0.2076436613 & -0.5\\\\\n",
       "\t0-1993 & 0 & 1993 &  0.010772713 &  0.0018245193 &  0.0 & -0.15539469 &  0.1027742980 &  0.078164773 &  0.013478690 &  0.04652002 &  0.015192971 &  0.0000000000 &  0.5\\\\\n",
       "\t0-1994 & 0 & 1994 &  0.034959773 &  0.0388160427 &  0.0 & -0.02806837 &  0.0898896168 &  0.029631798 &  0.006160184 &  0.12783337 &  0.118167799 &  0.0000000000 & -0.5\\\\\n",
       "\t0-1995 & 0 & 1995 &  0.031159245 &  0.0255089921 & -0.5 & -0.01487016 &  0.0913040437 &  0.059049029 & -0.001024066 &  0.14842001 &  0.008353525 &  0.0000000000 &  0.5\\\\\n",
       "\t0-1996 & 0 & 1996 &  0.015857216 &  0.0017373256 &  0.5 &  0.19217123 & -0.1259988250 &  0.060084811 &  0.001024066 &  0.09844007 & -0.026863215 &  0.0868586003 &  0.0\\\\\n",
       "\t0-1997 & 0 & 1997 &  0.007429288 & -0.0068458719 &  0.0 & -0.01512484 & -0.0896433402 &  0.044357853 &  0.018256085 &  0.06062462 & -0.256382432 &  0.0000000000 & -0.5\\\\\n",
       "\t0-1998 & 0 & 1998 & -0.013352105 & -0.0308981561 &  0.0 & -0.33871590 & -0.0915822665 &  0.147635999 &  0.006012042 &  0.00000000 & -0.103444736 &  0.0000000000 &  0.5\\\\\n",
       "\t0-1999 & 0 & 1999 &  0.024479115 &  0.0199589931 & -0.5 &  0.34363431 &  0.1051332959 &  0.123904093 & -0.003001503 &  0.18721154 &  0.316258976 &  0.0000000000 &  0.0\\\\\n",
       "\t0-2000 & 0 & 2000 &  0.010736315 &  0.0051233463 &  0.5 &  0.45901574 &  0.0628943640 &  0.090286847 & -0.007038742 &  0.13657554 & -0.310307387 &  0.0195966413 &  0.0\\\\\n",
       "\t0-2001 & 0 & 2001 & -0.012860210 & -0.0130581957 & -0.5 & -0.10950287 & -0.1242445967 &  0.084129531 & -0.007088637 &  0.00000000 & -0.261116345 &  0.0000000000 &  0.0\\\\\n",
       "\t0-2002 & 0 & 2002 &  0.014477534 &  0.0245535667 &  0.5 &  0.03522143 & -0.0377329263 &  0.078820960 & -0.009188426 &  0.06187540 & -0.196926170 &  0.0000000000 &  0.0\\\\\n",
       "\t0-2004 & 0 & 2004 &  0.013203359 & -0.0004923830 & -0.5 &  0.20097118 &  0.0890274168 &  0.082655722 &  0.000000000 & -0.01869213 &  0.073310952 &  0.0000000000 &  0.0\\\\\n",
       "\t0-2005 & 0 & 2005 & -0.004302008 &  0.0087232322 &  0.5 &  0.31611580 & -0.0094022598 &  0.044905504 & -0.002059733 & -0.12014431 &  0.341250994 &  0.2223047186 & -0.5\\\\\n",
       "\t0-2006 & 0 & 2006 &  0.002756154 & -0.0097746472 &  0.0 &  0.16065287 & -0.0484778223 &  0.002989539 &  0.002059733 & -0.06595797 &  0.063789817 &  0.0000000000 &  0.5\\\\\n",
       "\t0-2008 & 0 & 2008 & -0.072976198 & -0.0535455057 &  0.5 &  0.23562571 &  0.1064363396 &  0.049723435 &  0.014300550 & -0.05001042 & -0.561164650 &  0.0000000000 & -0.5\\\\\n",
       "\t0-2009 & 0 & 2009 & -0.018964601 & -0.0709879183 &  0.0 & -0.38749772 &  0.0376843968 &  0.118611879 & -0.014300550 &  0.02531781 &  0.188591815 & -0.9112279213 &  0.5\\\\\n",
       "\t0-2011 & 0 & 2011 & -0.027442840 &  0.0337387012 &  0.0 &  0.17035756 &  0.0914818714 &  0.061593011 & -0.002074690 &  0.00000000 & -0.188344934 &  0.0000000000 &  0.5\\\\\n",
       "\t0-2012 & 0 & 2012 & -0.012998704 &  0.0571105641 &  0.0 &  0.01671820 &  0.0146306523 &  0.049632624 & -0.001038961 & -0.10318424 &  0.207581791 &  0.9044428289 & -0.5\\\\\n",
       "\t0-2013 & 0 & 2013 & -0.007562834 & -0.1167735606 &  0.0 & -0.00462769 & -0.1828863972 &  0.028365790 &  0.004149384 & -0.06744128 &  0.445140246 &  0.0000000000 &  0.0\\\\\n",
       "\t1-2003 & 1 & 2003 & -0.009538650 &  0.0039634439 &  0.0 &  0.09713002 &  0.0843496712 &  0.075329719 & -0.003081667 &  0.07696104 &  0.221828367 &  0.0170148206 &  0.0\\\\\n",
       "\t1-2007 & 1 & 2007 & -0.017173710 &  0.0245622798 & -0.5 &  0.04150997 & -0.0002505979 &  0.014815086 &  0.000000000 & -0.07061757 & -0.118048778 &  0.0000000000 &  0.0\\\\\n",
       "\t1-2010 & 1 & 2010 &  0.042285908 &  0.0455902315 &  0.0 &  0.21075852 &  0.0715071253 &  0.054808236 & -0.007227703 &  0.24294618 & -0.023341674 &  0.0000000000 & -0.5\\\\\n",
       "\t1-2014 & 1 & 2014 & -0.032698672 & -0.0364950713 &  0.0 & -0.06275503 & -0.0603498861 &  0.019588603 &  0.026559273 & -0.07232066 &  0.042192672 &  0.0002122224 &  0.5\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A pdata.frame: 28 × 13\n",
       "\n",
       "| <!--/--> | id &lt;fct&gt; | time &lt;fct&gt; | Y1 &lt;dbl&gt; | Y2 &lt;dbl&gt; | Y3 &lt;dbl&gt; | Y4 &lt;dbl&gt; | Y5 &lt;dbl&gt; | Y6 &lt;dbl&gt; | Y7 &lt;dbl&gt; | Y8 &lt;dbl&gt; | Y9 &lt;dbl&gt; | Y10 &lt;dbl&gt; | Y11 &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 0-1987 | 0 | 1987 |  0.046607215 | -0.0002078956 |  0.0 |  0.14329221 |  0.1707078336 | -0.011862535 |  0.000000000 |  0.07410797 |  0.142432215 |  0.0000000000 |  0.0 |\n",
       "| 0-1988 | 0 | 1988 |  0.054685886 |  0.0659408662 |  0.0 | -0.27232247 |  0.1875246945 | -0.036456042 |  0.006960585 |  0.00000000 |  0.328497539 |  0.0000000000 |  0.0 |\n",
       "| 0-1989 | 0 | 1989 |  0.034524371 |  0.0250528266 |  0.0 |  0.19834206 |  0.0005499123 | -0.042990185 |  0.022858138 | -0.11332869 |  0.232060601 |  0.0000000000 |  0.0 |\n",
       "| 0-1990 | 0 | 1990 |  0.038998671 |  0.0696834290 |  0.0 |  0.21128783 |  0.0213534086 | -0.050341755 |  0.030052345 | -0.08338161 | -0.519724355 | -0.0870144800 |  0.0 |\n",
       "| 0-1991 | 0 | 1991 |  0.014394372 |  0.0108691005 | -0.5 | -0.15698568 |  0.1077704117 | -0.016438726 |  0.033426293 | -0.09097178 | -0.070368088 |  0.0000000000 |  0.0 |\n",
       "| 0-1992 | 0 | 1992 |  0.008714600 |  0.0088384495 |  0.5 | -0.03704127 |  0.0555237346 |  0.019152432 |  0.015781495 |  0.00000000 | -0.321780974 | -0.2076436613 | -0.5 |\n",
       "| 0-1993 | 0 | 1993 |  0.010772713 |  0.0018245193 |  0.0 | -0.15539469 |  0.1027742980 |  0.078164773 |  0.013478690 |  0.04652002 |  0.015192971 |  0.0000000000 |  0.5 |\n",
       "| 0-1994 | 0 | 1994 |  0.034959773 |  0.0388160427 |  0.0 | -0.02806837 |  0.0898896168 |  0.029631798 |  0.006160184 |  0.12783337 |  0.118167799 |  0.0000000000 | -0.5 |\n",
       "| 0-1995 | 0 | 1995 |  0.031159245 |  0.0255089921 | -0.5 | -0.01487016 |  0.0913040437 |  0.059049029 | -0.001024066 |  0.14842001 |  0.008353525 |  0.0000000000 |  0.5 |\n",
       "| 0-1996 | 0 | 1996 |  0.015857216 |  0.0017373256 |  0.5 |  0.19217123 | -0.1259988250 |  0.060084811 |  0.001024066 |  0.09844007 | -0.026863215 |  0.0868586003 |  0.0 |\n",
       "| 0-1997 | 0 | 1997 |  0.007429288 | -0.0068458719 |  0.0 | -0.01512484 | -0.0896433402 |  0.044357853 |  0.018256085 |  0.06062462 | -0.256382432 |  0.0000000000 | -0.5 |\n",
       "| 0-1998 | 0 | 1998 | -0.013352105 | -0.0308981561 |  0.0 | -0.33871590 | -0.0915822665 |  0.147635999 |  0.006012042 |  0.00000000 | -0.103444736 |  0.0000000000 |  0.5 |\n",
       "| 0-1999 | 0 | 1999 |  0.024479115 |  0.0199589931 | -0.5 |  0.34363431 |  0.1051332959 |  0.123904093 | -0.003001503 |  0.18721154 |  0.316258976 |  0.0000000000 |  0.0 |\n",
       "| 0-2000 | 0 | 2000 |  0.010736315 |  0.0051233463 |  0.5 |  0.45901574 |  0.0628943640 |  0.090286847 | -0.007038742 |  0.13657554 | -0.310307387 |  0.0195966413 |  0.0 |\n",
       "| 0-2001 | 0 | 2001 | -0.012860210 | -0.0130581957 | -0.5 | -0.10950287 | -0.1242445967 |  0.084129531 | -0.007088637 |  0.00000000 | -0.261116345 |  0.0000000000 |  0.0 |\n",
       "| 0-2002 | 0 | 2002 |  0.014477534 |  0.0245535667 |  0.5 |  0.03522143 | -0.0377329263 |  0.078820960 | -0.009188426 |  0.06187540 | -0.196926170 |  0.0000000000 |  0.0 |\n",
       "| 0-2004 | 0 | 2004 |  0.013203359 | -0.0004923830 | -0.5 |  0.20097118 |  0.0890274168 |  0.082655722 |  0.000000000 | -0.01869213 |  0.073310952 |  0.0000000000 |  0.0 |\n",
       "| 0-2005 | 0 | 2005 | -0.004302008 |  0.0087232322 |  0.5 |  0.31611580 | -0.0094022598 |  0.044905504 | -0.002059733 | -0.12014431 |  0.341250994 |  0.2223047186 | -0.5 |\n",
       "| 0-2006 | 0 | 2006 |  0.002756154 | -0.0097746472 |  0.0 |  0.16065287 | -0.0484778223 |  0.002989539 |  0.002059733 | -0.06595797 |  0.063789817 |  0.0000000000 |  0.5 |\n",
       "| 0-2008 | 0 | 2008 | -0.072976198 | -0.0535455057 |  0.5 |  0.23562571 |  0.1064363396 |  0.049723435 |  0.014300550 | -0.05001042 | -0.561164650 |  0.0000000000 | -0.5 |\n",
       "| 0-2009 | 0 | 2009 | -0.018964601 | -0.0709879183 |  0.0 | -0.38749772 |  0.0376843968 |  0.118611879 | -0.014300550 |  0.02531781 |  0.188591815 | -0.9112279213 |  0.5 |\n",
       "| 0-2011 | 0 | 2011 | -0.027442840 |  0.0337387012 |  0.0 |  0.17035756 |  0.0914818714 |  0.061593011 | -0.002074690 |  0.00000000 | -0.188344934 |  0.0000000000 |  0.5 |\n",
       "| 0-2012 | 0 | 2012 | -0.012998704 |  0.0571105641 |  0.0 |  0.01671820 |  0.0146306523 |  0.049632624 | -0.001038961 | -0.10318424 |  0.207581791 |  0.9044428289 | -0.5 |\n",
       "| 0-2013 | 0 | 2013 | -0.007562834 | -0.1167735606 |  0.0 | -0.00462769 | -0.1828863972 |  0.028365790 |  0.004149384 | -0.06744128 |  0.445140246 |  0.0000000000 |  0.0 |\n",
       "| 1-2003 | 1 | 2003 | -0.009538650 |  0.0039634439 |  0.0 |  0.09713002 |  0.0843496712 |  0.075329719 | -0.003081667 |  0.07696104 |  0.221828367 |  0.0170148206 |  0.0 |\n",
       "| 1-2007 | 1 | 2007 | -0.017173710 |  0.0245622798 | -0.5 |  0.04150997 | -0.0002505979 |  0.014815086 |  0.000000000 | -0.07061757 | -0.118048778 |  0.0000000000 |  0.0 |\n",
       "| 1-2010 | 1 | 2010 |  0.042285908 |  0.0455902315 |  0.0 |  0.21075852 |  0.0715071253 |  0.054808236 | -0.007227703 |  0.24294618 | -0.023341674 |  0.0000000000 | -0.5 |\n",
       "| 1-2014 | 1 | 2014 | -0.032698672 | -0.0364950713 |  0.0 | -0.06275503 | -0.0603498861 |  0.019588603 |  0.026559273 | -0.07232066 |  0.042192672 |  0.0002122224 |  0.5 |\n",
       "\n"
      ],
      "text/plain": [
       "       id time Y1           Y2            Y3   Y4          Y5           \n",
       "0-1987 0  1987  0.046607215 -0.0002078956  0.0  0.14329221  0.1707078336\n",
       "0-1988 0  1988  0.054685886  0.0659408662  0.0 -0.27232247  0.1875246945\n",
       "0-1989 0  1989  0.034524371  0.0250528266  0.0  0.19834206  0.0005499123\n",
       "0-1990 0  1990  0.038998671  0.0696834290  0.0  0.21128783  0.0213534086\n",
       "0-1991 0  1991  0.014394372  0.0108691005 -0.5 -0.15698568  0.1077704117\n",
       "0-1992 0  1992  0.008714600  0.0088384495  0.5 -0.03704127  0.0555237346\n",
       "0-1993 0  1993  0.010772713  0.0018245193  0.0 -0.15539469  0.1027742980\n",
       "0-1994 0  1994  0.034959773  0.0388160427  0.0 -0.02806837  0.0898896168\n",
       "0-1995 0  1995  0.031159245  0.0255089921 -0.5 -0.01487016  0.0913040437\n",
       "0-1996 0  1996  0.015857216  0.0017373256  0.5  0.19217123 -0.1259988250\n",
       "0-1997 0  1997  0.007429288 -0.0068458719  0.0 -0.01512484 -0.0896433402\n",
       "0-1998 0  1998 -0.013352105 -0.0308981561  0.0 -0.33871590 -0.0915822665\n",
       "0-1999 0  1999  0.024479115  0.0199589931 -0.5  0.34363431  0.1051332959\n",
       "0-2000 0  2000  0.010736315  0.0051233463  0.5  0.45901574  0.0628943640\n",
       "0-2001 0  2001 -0.012860210 -0.0130581957 -0.5 -0.10950287 -0.1242445967\n",
       "0-2002 0  2002  0.014477534  0.0245535667  0.5  0.03522143 -0.0377329263\n",
       "0-2004 0  2004  0.013203359 -0.0004923830 -0.5  0.20097118  0.0890274168\n",
       "0-2005 0  2005 -0.004302008  0.0087232322  0.5  0.31611580 -0.0094022598\n",
       "0-2006 0  2006  0.002756154 -0.0097746472  0.0  0.16065287 -0.0484778223\n",
       "0-2008 0  2008 -0.072976198 -0.0535455057  0.5  0.23562571  0.1064363396\n",
       "0-2009 0  2009 -0.018964601 -0.0709879183  0.0 -0.38749772  0.0376843968\n",
       "0-2011 0  2011 -0.027442840  0.0337387012  0.0  0.17035756  0.0914818714\n",
       "0-2012 0  2012 -0.012998704  0.0571105641  0.0  0.01671820  0.0146306523\n",
       "0-2013 0  2013 -0.007562834 -0.1167735606  0.0 -0.00462769 -0.1828863972\n",
       "1-2003 1  2003 -0.009538650  0.0039634439  0.0  0.09713002  0.0843496712\n",
       "1-2007 1  2007 -0.017173710  0.0245622798 -0.5  0.04150997 -0.0002505979\n",
       "1-2010 1  2010  0.042285908  0.0455902315  0.0  0.21075852  0.0715071253\n",
       "1-2014 1  2014 -0.032698672 -0.0364950713  0.0 -0.06275503 -0.0603498861\n",
       "       Y6           Y7           Y8          Y9           Y10           Y11 \n",
       "0-1987 -0.011862535  0.000000000  0.07410797  0.142432215  0.0000000000  0.0\n",
       "0-1988 -0.036456042  0.006960585  0.00000000  0.328497539  0.0000000000  0.0\n",
       "0-1989 -0.042990185  0.022858138 -0.11332869  0.232060601  0.0000000000  0.0\n",
       "0-1990 -0.050341755  0.030052345 -0.08338161 -0.519724355 -0.0870144800  0.0\n",
       "0-1991 -0.016438726  0.033426293 -0.09097178 -0.070368088  0.0000000000  0.0\n",
       "0-1992  0.019152432  0.015781495  0.00000000 -0.321780974 -0.2076436613 -0.5\n",
       "0-1993  0.078164773  0.013478690  0.04652002  0.015192971  0.0000000000  0.5\n",
       "0-1994  0.029631798  0.006160184  0.12783337  0.118167799  0.0000000000 -0.5\n",
       "0-1995  0.059049029 -0.001024066  0.14842001  0.008353525  0.0000000000  0.5\n",
       "0-1996  0.060084811  0.001024066  0.09844007 -0.026863215  0.0868586003  0.0\n",
       "0-1997  0.044357853  0.018256085  0.06062462 -0.256382432  0.0000000000 -0.5\n",
       "0-1998  0.147635999  0.006012042  0.00000000 -0.103444736  0.0000000000  0.5\n",
       "0-1999  0.123904093 -0.003001503  0.18721154  0.316258976  0.0000000000  0.0\n",
       "0-2000  0.090286847 -0.007038742  0.13657554 -0.310307387  0.0195966413  0.0\n",
       "0-2001  0.084129531 -0.007088637  0.00000000 -0.261116345  0.0000000000  0.0\n",
       "0-2002  0.078820960 -0.009188426  0.06187540 -0.196926170  0.0000000000  0.0\n",
       "0-2004  0.082655722  0.000000000 -0.01869213  0.073310952  0.0000000000  0.0\n",
       "0-2005  0.044905504 -0.002059733 -0.12014431  0.341250994  0.2223047186 -0.5\n",
       "0-2006  0.002989539  0.002059733 -0.06595797  0.063789817  0.0000000000  0.5\n",
       "0-2008  0.049723435  0.014300550 -0.05001042 -0.561164650  0.0000000000 -0.5\n",
       "0-2009  0.118611879 -0.014300550  0.02531781  0.188591815 -0.9112279213  0.5\n",
       "0-2011  0.061593011 -0.002074690  0.00000000 -0.188344934  0.0000000000  0.5\n",
       "0-2012  0.049632624 -0.001038961 -0.10318424  0.207581791  0.9044428289 -0.5\n",
       "0-2013  0.028365790  0.004149384 -0.06744128  0.445140246  0.0000000000  0.0\n",
       "1-2003  0.075329719 -0.003081667  0.07696104  0.221828367  0.0170148206  0.0\n",
       "1-2007  0.014815086  0.000000000 -0.07061757 -0.118048778  0.0000000000  0.0\n",
       "1-2010  0.054808236 -0.007227703  0.24294618 -0.023341674  0.0000000000 -0.5\n",
       "1-2014  0.019588603  0.026559273 -0.07232066  0.042192672  0.0002122224  0.5"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#パネルデータの生成\n",
    "index <- adjusted[, 1:2] %>% apply(2, as.character) %>% as.data.frame\n",
    "panel <- bind_cols(index, relation) %>% pdata.frame(index = c(\"id\", \"time\"))\n",
    "#目視確認\n",
    "panel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       " id          time          Y1                  Y2                  Y3      \n",
       " 0:24   1987   : 1   Min.   :-0.072976   Min.   :-0.116774   Min.   :-0.5  \n",
       " 1: 4   1988   : 1   1st Qu.:-0.012895   1st Qu.:-0.007578   1st Qu.: 0.0  \n",
       "        1989   : 1   Median : 0.009725   Median : 0.006923   Median : 0.0  \n",
       "        1990   : 1   Mean   : 0.006292   Mean   : 0.004733   Mean   : 0.0  \n",
       "        1991   : 1   3rd Qu.: 0.026149   3rd Qu.: 0.025167   3rd Qu.: 0.0  \n",
       "        1992   : 1   Max.   : 0.054686   Max.   : 0.069683   Max.   : 0.5  \n",
       "        (Other):22                                                         \n",
       "       Y4                 Y5                 Y6                 Y7           \n",
       " Min.   :-0.38750   Min.   :-0.18289   Min.   :-0.05034   Min.   :-0.014301  \n",
       " 1st Qu.:-0.04347   1st Qu.:-0.04042   1st Qu.: 0.01807   1st Qu.:-0.002306  \n",
       " Median : 0.03837   Median : 0.04660   Median : 0.04968   Median : 0.000512  \n",
       " Mean   : 0.05178   Mean   : 0.02571   Mean   : 0.04501   Mean   : 0.005141  \n",
       " 3rd Qu.: 0.19900   3rd Qu.: 0.09135   3rd Qu.: 0.07833   3rd Qu.: 0.013684  \n",
       " Max.   : 0.45902   Max.   : 0.18752   Max.   : 0.14764   Max.   : 0.033426  \n",
       "                                                                             \n",
       "       Y8                 Y9                 Y10                 Y11        \n",
       " Min.   :-0.12014   Min.   :-0.561165   Min.   :-0.911228   Min.   :-0.500  \n",
       " 1st Qu.:-0.06824   1st Qu.:-0.190490   1st Qu.: 0.000000   1st Qu.:-0.125  \n",
       " Median : 0.00000   Median : 0.011773   Median : 0.000000   Median : 0.000  \n",
       " Mean   : 0.01539   Mean   :-0.007613   Mean   : 0.001591   Mean   : 0.000  \n",
       " 3rd Qu.: 0.07482   3rd Qu.: 0.193339   3rd Qu.: 0.000000   3rd Qu.: 0.125  \n",
       " Max.   : 0.24295   Max.   : 0.445140   Max.   : 0.904443   Max.   : 0.500  \n",
       "                                                                            "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#要約統計量を求める。\n",
    "panel %>%\n",
    "  summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in pvargmm(dependent_vars = c(\"Y1\", \"Y2\", \"Y3\", \"Y4\", \"Y5\", \"Y6\", :\n",
      "\"The matrix Lambda is singular, therefore the general inverse is used\"\n",
      "\n",
      " One-step estimation: Matrix inversion\n",
      "                                                                              \n",
      "\n",
      "Warning message in pvargmm(dependent_vars = c(\"Y1\", \"Y2\", \"Y3\", \"Y4\", \"Y5\", \"Y6\", :\n",
      "\"The matrix D_e is singular, therefore the general inverse is used\"\n",
      "Warning message in sqrt(diag(var_first_step)):\n",
      "\" 計算結果が NaN になりました \"\n",
      "\n",
      "Windmeijer - Sigmund robust se: [===========================] Iteration 2 of 2\n",
      "                                                                              \n",
      "\n"
     ]
    }
   ],
   "source": [
    "#モデルの形成\n",
    "model <- pvargmm(dependent_vars = c(\"Y1\", \"Y2\", \"Y3\", \"Y4\", \"Y5\", \"Y6\", \"Y7\", \"Y8\", \"Y9\", \"Y10\", \"Y11\"), \n",
    "                 lags = 1,\n",
    "                 transformation = c(\"fod\"),\n",
    "                 data = panel,\n",
    "                 panel_identifier =  c(\"id\", \"time\"), \n",
    "                 steps = c(\"twostep\"),\n",
    "                 system_instruments = F,\n",
    "                 max_instr_dependent_vars = 2,\n",
    "                 min_instr_dependent_vars = 1L\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "---------------------------------------------------\n",
       "Dynamic Panel VAR estimation, two-step GMM \n",
       "---------------------------------------------------\n",
       "Transformation: Forward orthogonal deviations \n",
       "Group variable: id \n",
       "Time variable: time \n",
       "Number of observations = 16 \n",
       "Number of groups = 2 \n",
       "Obs per group: min = 16 \n",
       "               avg = 16 \n",
       "               max = 16 \n",
       "Number of instruments = 6171 \n",
       "\n",
       "======================================================================================================================\n",
       "          Y1        Y2        Y3        Y4        Y5        Y6        Y7        Y8        Y9        Y10       Y11     \n",
       "----------------------------------------------------------------------------------------------------------------------\n",
       "lag1_Y1   -0.0028   -0.0062    0.2220   -0.0480   -0.0713    0.0020   -0.0032    0.0356   -0.2040    0.0030    0.0694 \n",
       "          (0.0029)  (0.0064)  (0.2297)  (0.0497)  (0.0737)  (0.0021)  (0.0033)  (0.0368)  (0.2110)  (0.0031)  (0.0718)\n",
       "lag1_Y2   -0.0050    0.0275    0.0665   -0.0711    0.0470    0.0084    0.0002    0.0002    0.0185    0.3901   -0.0490 \n",
       "          (0.0052)  (0.0285)  (0.0688)  (0.0736)  (0.0486)  (0.0086)  (0.0003)  (0.0002)  (0.0191)  (0.4036)  (0.0506)\n",
       "lag1_Y3   -0.1693   -0.0997    1.1328    0.3829   -0.9092    0.0764    0.0159   -0.6283   -0.1379    1.2017   -3.0932 \n",
       "          (0.1752)  (0.1031)  (1.1720)  (0.3962)  (0.9407)  (0.0791)  (0.0164)  (0.6501)  (0.1427)  (1.2433)  (3.2002)\n",
       "lag1_Y4   -0.1018   -0.2031    0.1416    0.4216   -0.7854    0.0740   -0.0072   -0.3813   -0.7137   -1.1339   -0.3781 \n",
       "          (0.1053)  (0.2101)  (0.1465)  (0.4362)  (0.8126)  (0.0765)  (0.0075)  (0.3945)  (0.7384)  (1.1731)  (0.3911)\n",
       "lag1_Y5    0.0010   -0.0201    0.2714    0.1218   -0.1988    0.0155   -0.0015    0.0306   -0.2511   -0.1497    0.3809 \n",
       "          (0.0010)  (0.0208)  (0.2808)  (0.1260)  (0.2057)  (0.0160)  (0.0016)  (0.0316)  (0.2598)  (0.1548)  (0.3941)\n",
       "lag1_Y6    0.0185    0.0151   -0.4520    0.0361    0.1133   -0.0066   -0.0019    0.0210   -0.0355   -0.2796    0.3409 \n",
       "          (0.0191)  (0.0156)  (0.4676)  (0.0373)  (0.1173)  (0.0068)  (0.0019)  (0.0218)  (0.0367)  (0.2893)  (0.3527)\n",
       "lag1_Y7   -0.0003    0.0018    0.0511   -0.0136    0.0153   -0.0011    0.0011   -0.0023    0.0392    0.0517   -0.0450 \n",
       "          (0.0003)  (0.0019)  (0.0528)  (0.0141)  (0.0158)  (0.0011)  (0.0012)  (0.0024)  (0.0405)  (0.0535)  (0.0465)\n",
       "lag1_Y8    0.0099   -0.0455    0.1571    0.3401   -0.2181   -0.0049   -0.0012    0.0360   -0.8514   -0.9308    0.3010 \n",
       "          (0.0103)  (0.0471)  (0.1626)  (0.3519)  (0.2257)  (0.0051)  (0.0012)  (0.0372)  (0.8809)  (0.9630)  (0.3114)\n",
       "lag1_Y9    0.0137   -0.0172    0.1554   -1.0944    0.2983   -0.0133   -0.0235    0.2952    1.5217    0.7878    0.7928 \n",
       "          (0.0142)  (0.0178)  (0.1608)  (1.1323)  (0.3086)  (0.0138)  (0.0243)  (0.3054)  (1.5743)  (0.8150)  (0.8203)\n",
       "lag1_Y10  -0.0772    0.1379   -1.5153   -0.4357    0.3174    0.0684    0.0029   -0.4685    1.6201    2.3678   -1.2263 \n",
       "          (0.0799)  (0.1427)  (1.5677)  (0.4508)  (0.3284)  (0.0708)  (0.0030)  (0.4847)  (1.6762)  (2.4498)  (1.2688)\n",
       "lag1_Y11   0.0971    0.0105   -0.2774   -0.0540   -0.1086   -0.1045   -0.0170    0.3311   -0.9623   -1.6206    0.6966 \n",
       "          (0.1004)  (0.0108)  (0.2870)  (0.0559)  (0.1124)  (0.1081)  (0.0176)  (0.3425)  (0.9956)  (1.6767)  (0.7207)\n",
       "======================================================================================================================\n",
       "*** p < 0.001; ** p < 0.01; * p < 0.05\n",
       "\n",
       "---------------------------------------------------\n",
       "Instruments for  equation\n",
       " Standard\n",
       "  \n",
       " GMM-type\n",
       "  Dependent vars: L(1, 2)\n",
       "  Collapse =  FALSE \n",
       "---------------------------------------------------\n",
       "\n",
       "Hansen test of overid. restrictions: chi2(6050) = 0 Prob > chi2 = 1\n",
       "(Robust, but weakened by many instruments.)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#詳細結果の目視確認\n",
    "model %>%\n",
    "  summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 11 × 11</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>fod_lag1_Y1</th><th scope=col>fod_lag1_Y2</th><th scope=col>fod_lag1_Y3</th><th scope=col>fod_lag1_Y4</th><th scope=col>fod_lag1_Y5</th><th scope=col>fod_lag1_Y6</th><th scope=col>fod_lag1_Y7</th><th scope=col>fod_lag1_Y8</th><th scope=col>fod_lag1_Y9</th><th scope=col>fod_lag1_Y10</th><th scope=col>fod_lag1_Y11</th></tr>\n",
       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>-0.002754814</td><td>-0.0050207731</td><td>-0.16931355</td><td>-0.101767386</td><td> 0.0009635191</td><td> 0.018465865</td><td>-0.000277476</td><td> 0.009914315</td><td> 0.01369284</td><td>-0.077209456</td><td> 0.09705811</td></tr>\n",
       "\t<tr><td>-0.006194944</td><td> 0.0275331959</td><td>-0.09965131</td><td>-0.203053059</td><td>-0.0201301044</td><td> 0.015078519</td><td> 0.001803873</td><td>-0.045506817</td><td>-0.01724195</td><td> 0.137897058</td><td> 0.01045771</td></tr>\n",
       "\t<tr><td> 0.221998205</td><td> 0.0665371594</td><td> 1.13276540</td><td> 0.141613520</td><td> 0.2713809097</td><td>-0.451971770</td><td> 0.051054720</td><td> 0.157145396</td><td> 0.15541821</td><td>-1.515289796</td><td>-0.27740094</td></tr>\n",
       "\t<tr><td>-0.048013497</td><td>-0.0711154434</td><td> 0.38290726</td><td> 0.421601401</td><td> 0.1218240345</td><td> 0.036099177</td><td>-0.013596904</td><td> 0.340133932</td><td>-1.09443388</td><td>-0.435705360</td><td>-0.05399765</td></tr>\n",
       "\t<tr><td>-0.071276317</td><td> 0.0469656463</td><td>-0.90920774</td><td>-0.785439391</td><td>-0.1987877581</td><td> 0.113328499</td><td> 0.015299109</td><td>-0.218142119</td><td> 0.29830932</td><td> 0.317433630</td><td>-0.10863080</td></tr>\n",
       "\t<tr><td> 0.002014289</td><td> 0.0083530336</td><td> 0.07643555</td><td> 0.073980730</td><td> 0.0154726499</td><td>-0.006572455</td><td>-0.001071100</td><td>-0.004922137</td><td>-0.01333102</td><td> 0.068396337</td><td>-0.10447733</td></tr>\n",
       "\t<tr><td>-0.003231959</td><td> 0.0002458912</td><td> 0.01588652</td><td>-0.007247966</td><td>-0.0015249276</td><td>-0.001877811</td><td> 0.001117015</td><td>-0.001197679</td><td>-0.02346813</td><td> 0.002867625</td><td>-0.01703424</td></tr>\n",
       "\t<tr><td> 0.035585042</td><td> 0.0001842591</td><td>-0.62832877</td><td>-0.381341194</td><td> 0.0305651042</td><td> 0.021040679</td><td>-0.002323263</td><td> 0.035959203</td><td> 0.29516834</td><td>-0.468529608</td><td> 0.33108207</td></tr>\n",
       "\t<tr><td>-0.203961116</td><td> 0.0184879870</td><td>-0.13790997</td><td>-0.713682607</td><td>-0.2511080834</td><td>-0.035477337</td><td> 0.039158270</td><td>-0.851441122</td><td> 1.52167667</td><td> 1.620137791</td><td>-0.96230890</td></tr>\n",
       "\t<tr><td> 0.002956033</td><td> 0.3900771454</td><td> 1.20173723</td><td>-1.133861550</td><td>-0.1496662924</td><td>-0.279649171</td><td> 0.051722756</td><td>-0.930756371</td><td> 0.78775431</td><td> 2.367845228</td><td>-1.62063518</td></tr>\n",
       "\t<tr><td> 0.069354284</td><td>-0.0489537994</td><td>-3.09317304</td><td>-0.378061683</td><td> 0.3809309915</td><td> 0.340857777</td><td>-0.044992223</td><td> 0.301032474</td><td> 0.79282291</td><td>-1.226322249</td><td> 0.69663569</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 11 × 11\n",
       "\\begin{tabular}{lllllllllll}\n",
       " fod\\_lag1\\_Y1 & fod\\_lag1\\_Y2 & fod\\_lag1\\_Y3 & fod\\_lag1\\_Y4 & fod\\_lag1\\_Y5 & fod\\_lag1\\_Y6 & fod\\_lag1\\_Y7 & fod\\_lag1\\_Y8 & fod\\_lag1\\_Y9 & fod\\_lag1\\_Y10 & fod\\_lag1\\_Y11\\\\\n",
       " <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t -0.002754814 & -0.0050207731 & -0.16931355 & -0.101767386 &  0.0009635191 &  0.018465865 & -0.000277476 &  0.009914315 &  0.01369284 & -0.077209456 &  0.09705811\\\\\n",
       "\t -0.006194944 &  0.0275331959 & -0.09965131 & -0.203053059 & -0.0201301044 &  0.015078519 &  0.001803873 & -0.045506817 & -0.01724195 &  0.137897058 &  0.01045771\\\\\n",
       "\t  0.221998205 &  0.0665371594 &  1.13276540 &  0.141613520 &  0.2713809097 & -0.451971770 &  0.051054720 &  0.157145396 &  0.15541821 & -1.515289796 & -0.27740094\\\\\n",
       "\t -0.048013497 & -0.0711154434 &  0.38290726 &  0.421601401 &  0.1218240345 &  0.036099177 & -0.013596904 &  0.340133932 & -1.09443388 & -0.435705360 & -0.05399765\\\\\n",
       "\t -0.071276317 &  0.0469656463 & -0.90920774 & -0.785439391 & -0.1987877581 &  0.113328499 &  0.015299109 & -0.218142119 &  0.29830932 &  0.317433630 & -0.10863080\\\\\n",
       "\t  0.002014289 &  0.0083530336 &  0.07643555 &  0.073980730 &  0.0154726499 & -0.006572455 & -0.001071100 & -0.004922137 & -0.01333102 &  0.068396337 & -0.10447733\\\\\n",
       "\t -0.003231959 &  0.0002458912 &  0.01588652 & -0.007247966 & -0.0015249276 & -0.001877811 &  0.001117015 & -0.001197679 & -0.02346813 &  0.002867625 & -0.01703424\\\\\n",
       "\t  0.035585042 &  0.0001842591 & -0.62832877 & -0.381341194 &  0.0305651042 &  0.021040679 & -0.002323263 &  0.035959203 &  0.29516834 & -0.468529608 &  0.33108207\\\\\n",
       "\t -0.203961116 &  0.0184879870 & -0.13790997 & -0.713682607 & -0.2511080834 & -0.035477337 &  0.039158270 & -0.851441122 &  1.52167667 &  1.620137791 & -0.96230890\\\\\n",
       "\t  0.002956033 &  0.3900771454 &  1.20173723 & -1.133861550 & -0.1496662924 & -0.279649171 &  0.051722756 & -0.930756371 &  0.78775431 &  2.367845228 & -1.62063518\\\\\n",
       "\t  0.069354284 & -0.0489537994 & -3.09317304 & -0.378061683 &  0.3809309915 &  0.340857777 & -0.044992223 &  0.301032474 &  0.79282291 & -1.226322249 &  0.69663569\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 11 × 11\n",
       "\n",
       "| fod_lag1_Y1 &lt;dbl&gt; | fod_lag1_Y2 &lt;dbl&gt; | fod_lag1_Y3 &lt;dbl&gt; | fod_lag1_Y4 &lt;dbl&gt; | fod_lag1_Y5 &lt;dbl&gt; | fod_lag1_Y6 &lt;dbl&gt; | fod_lag1_Y7 &lt;dbl&gt; | fod_lag1_Y8 &lt;dbl&gt; | fod_lag1_Y9 &lt;dbl&gt; | fod_lag1_Y10 &lt;dbl&gt; | fod_lag1_Y11 &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| -0.002754814 | -0.0050207731 | -0.16931355 | -0.101767386 |  0.0009635191 |  0.018465865 | -0.000277476 |  0.009914315 |  0.01369284 | -0.077209456 |  0.09705811 |\n",
       "| -0.006194944 |  0.0275331959 | -0.09965131 | -0.203053059 | -0.0201301044 |  0.015078519 |  0.001803873 | -0.045506817 | -0.01724195 |  0.137897058 |  0.01045771 |\n",
       "|  0.221998205 |  0.0665371594 |  1.13276540 |  0.141613520 |  0.2713809097 | -0.451971770 |  0.051054720 |  0.157145396 |  0.15541821 | -1.515289796 | -0.27740094 |\n",
       "| -0.048013497 | -0.0711154434 |  0.38290726 |  0.421601401 |  0.1218240345 |  0.036099177 | -0.013596904 |  0.340133932 | -1.09443388 | -0.435705360 | -0.05399765 |\n",
       "| -0.071276317 |  0.0469656463 | -0.90920774 | -0.785439391 | -0.1987877581 |  0.113328499 |  0.015299109 | -0.218142119 |  0.29830932 |  0.317433630 | -0.10863080 |\n",
       "|  0.002014289 |  0.0083530336 |  0.07643555 |  0.073980730 |  0.0154726499 | -0.006572455 | -0.001071100 | -0.004922137 | -0.01333102 |  0.068396337 | -0.10447733 |\n",
       "| -0.003231959 |  0.0002458912 |  0.01588652 | -0.007247966 | -0.0015249276 | -0.001877811 |  0.001117015 | -0.001197679 | -0.02346813 |  0.002867625 | -0.01703424 |\n",
       "|  0.035585042 |  0.0001842591 | -0.62832877 | -0.381341194 |  0.0305651042 |  0.021040679 | -0.002323263 |  0.035959203 |  0.29516834 | -0.468529608 |  0.33108207 |\n",
       "| -0.203961116 |  0.0184879870 | -0.13790997 | -0.713682607 | -0.2511080834 | -0.035477337 |  0.039158270 | -0.851441122 |  1.52167667 |  1.620137791 | -0.96230890 |\n",
       "|  0.002956033 |  0.3900771454 |  1.20173723 | -1.133861550 | -0.1496662924 | -0.279649171 |  0.051722756 | -0.930756371 |  0.78775431 |  2.367845228 | -1.62063518 |\n",
       "|  0.069354284 | -0.0489537994 | -3.09317304 | -0.378061683 |  0.3809309915 |  0.340857777 | -0.044992223 |  0.301032474 |  0.79282291 | -1.226322249 |  0.69663569 |\n",
       "\n"
      ],
      "text/plain": [
       "   fod_lag1_Y1  fod_lag1_Y2   fod_lag1_Y3 fod_lag1_Y4  fod_lag1_Y5  \n",
       "1  -0.002754814 -0.0050207731 -0.16931355 -0.101767386  0.0009635191\n",
       "2  -0.006194944  0.0275331959 -0.09965131 -0.203053059 -0.0201301044\n",
       "3   0.221998205  0.0665371594  1.13276540  0.141613520  0.2713809097\n",
       "4  -0.048013497 -0.0711154434  0.38290726  0.421601401  0.1218240345\n",
       "5  -0.071276317  0.0469656463 -0.90920774 -0.785439391 -0.1987877581\n",
       "6   0.002014289  0.0083530336  0.07643555  0.073980730  0.0154726499\n",
       "7  -0.003231959  0.0002458912  0.01588652 -0.007247966 -0.0015249276\n",
       "8   0.035585042  0.0001842591 -0.62832877 -0.381341194  0.0305651042\n",
       "9  -0.203961116  0.0184879870 -0.13790997 -0.713682607 -0.2511080834\n",
       "10  0.002956033  0.3900771454  1.20173723 -1.133861550 -0.1496662924\n",
       "11  0.069354284 -0.0489537994 -3.09317304 -0.378061683  0.3809309915\n",
       "   fod_lag1_Y6  fod_lag1_Y7  fod_lag1_Y8  fod_lag1_Y9 fod_lag1_Y10 fod_lag1_Y11\n",
       "1   0.018465865 -0.000277476  0.009914315  0.01369284 -0.077209456  0.09705811 \n",
       "2   0.015078519  0.001803873 -0.045506817 -0.01724195  0.137897058  0.01045771 \n",
       "3  -0.451971770  0.051054720  0.157145396  0.15541821 -1.515289796 -0.27740094 \n",
       "4   0.036099177 -0.013596904  0.340133932 -1.09443388 -0.435705360 -0.05399765 \n",
       "5   0.113328499  0.015299109 -0.218142119  0.29830932  0.317433630 -0.10863080 \n",
       "6  -0.006572455 -0.001071100 -0.004922137 -0.01333102  0.068396337 -0.10447733 \n",
       "7  -0.001877811  0.001117015 -0.001197679 -0.02346813  0.002867625 -0.01703424 \n",
       "8   0.021040679 -0.002323263  0.035959203  0.29516834 -0.468529608  0.33108207 \n",
       "9  -0.035477337  0.039158270 -0.851441122  1.52167667  1.620137791 -0.96230890 \n",
       "10 -0.279649171  0.051722756 -0.930756371  0.78775431  2.367845228 -1.62063518 \n",
       "11  0.340857777 -0.044992223  0.301032474  0.79282291 -1.226322249  0.69663569 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#動的直接相関係数の抽出\n",
    "coefficient <- model %>%\n",
    "  coef %>%\n",
    "  apply(2, as.numeric) %>%\n",
    "  as.data.frame\n",
    "coefficient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "  fod_lag1_Y1          fod_lag1_Y2         fod_lag1_Y3        fod_lag1_Y4      \n",
       " Min.   :-0.2039611   Min.   :-0.071115   Min.   :-3.09317   Min.   :-1.13386  \n",
       " 1st Qu.:-0.0271042   1st Qu.:-0.002418   1st Qu.:-0.39882   1st Qu.:-0.54751  \n",
       " Median :-0.0027548   Median : 0.008353   Median :-0.09965   Median :-0.20305  \n",
       " Mean   :-0.0003204   Mean   : 0.039390   Mean   :-0.20253   Mean   :-0.27884  \n",
       " 3rd Qu.: 0.0192705   3rd Qu.: 0.037249   3rd Qu.: 0.22967   3rd Qu.: 0.03337  \n",
       " Max.   : 0.2219982   Max.   : 0.390077   Max.   : 1.20174   Max.   : 0.42160  \n",
       "  fod_lag1_Y5          fod_lag1_Y6        fod_lag1_Y7       \n",
       " Min.   :-0.2511081   Min.   :-0.45197   Min.   :-0.044992  \n",
       " 1st Qu.:-0.0848982   1st Qu.:-0.02102   1st Qu.:-0.001697  \n",
       " Median : 0.0009635   Median : 0.01508   Median : 0.001117  \n",
       " Mean   : 0.0181745   Mean   :-0.02097   Mean   : 0.008900  \n",
       " 3rd Qu.: 0.0761946   3rd Qu.: 0.02857   3rd Qu.: 0.027229  \n",
       " Max.   : 0.3809310   Max.   : 0.34086   Max.   : 0.051723  \n",
       "  fod_lag1_Y8         fod_lag1_Y9        fod_lag1_Y10        fod_lag1_Y11     \n",
       " Min.   :-0.930756   Min.   :-1.09443   Min.   :-1.515290   Min.   :-1.62064  \n",
       " 1st Qu.:-0.131825   1st Qu.:-0.01529   1st Qu.:-0.452118   1st Qu.:-0.19302  \n",
       " Median :-0.001198   Median : 0.15542   Median : 0.002868   Median :-0.05400  \n",
       " Mean   :-0.109798   Mean   : 0.24694   Mean   : 0.071957   Mean   :-0.18266  \n",
       " 3rd Qu.: 0.096552   3rd Qu.: 0.54303   3rd Qu.: 0.227665   3rd Qu.: 0.05376  \n",
       " Max.   : 0.340134   Max.   : 1.52168   Max.   : 2.367845   Max.   : 0.69664  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#要約統計量を求める。\n",
    "coefficient %>%\n",
    "  summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 11 × 11</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>V1</th><th scope=col>V2</th><th scope=col>V3</th><th scope=col>V4</th><th scope=col>V5</th><th scope=col>V6</th><th scope=col>V7</th><th scope=col>V8</th><th scope=col>V9</th><th scope=col>V10</th><th scope=col>V11</th></tr>\n",
       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>0.002850142</td><td>0.0051945133</td><td>0.17517252</td><td>0.105288975</td><td>0.000996861</td><td>0.019104863</td><td>0.0002870778</td><td>0.010257393</td><td>0.01416667</td><td>0.079881234</td><td>0.10041674</td></tr>\n",
       "\t<tr><td>0.006409316</td><td>0.0284859626</td><td>0.10309967</td><td>0.210079567</td><td>0.020826692</td><td>0.015600300</td><td>0.0018662949</td><td>0.047081548</td><td>0.01783860</td><td>0.142668888</td><td>0.01081959</td></tr>\n",
       "\t<tr><td>0.229680295</td><td>0.0688396307</td><td>1.17196394</td><td>0.146513956</td><td>0.280771854</td><td>0.467611933</td><td>0.0528214320</td><td>0.162583301</td><td>0.16079635</td><td>1.567725326</td><td>0.28700021</td></tr>\n",
       "\t<tr><td>0.049674970</td><td>0.0735763430</td><td>0.39615750</td><td>0.436190619</td><td>0.126039669</td><td>0.037348363</td><td>0.0140674157</td><td>0.351904026</td><td>1.13230599</td><td>0.450782635</td><td>0.05586620</td></tr>\n",
       "\t<tr><td>0.073742783</td><td>0.0485908592</td><td>0.94067023</td><td>0.812618964</td><td>0.205666668</td><td>0.117250151</td><td>0.0158285234</td><td>0.225690773</td><td>0.30863210</td><td>0.328418196</td><td>0.11238989</td></tr>\n",
       "\t<tr><td>0.002083992</td><td>0.0086420844</td><td>0.07908055</td><td>0.076540781</td><td>0.016008070</td><td>0.006799890</td><td>0.0011081651</td><td>0.005092464</td><td>0.01379233</td><td>0.070763144</td><td>0.10809270</td></tr>\n",
       "\t<tr><td>0.003343799</td><td>0.0002544001</td><td>0.01643626</td><td>0.007498777</td><td>0.001577697</td><td>0.001942791</td><td>0.0011556689</td><td>0.001239124</td><td>0.02428022</td><td>0.002966857</td><td>0.01762369</td></tr>\n",
       "\t<tr><td>0.036816438</td><td>0.0001906352</td><td>0.65007164</td><td>0.394537236</td><td>0.031622788</td><td>0.021768776</td><td>0.0024036582</td><td>0.037203545</td><td>0.30538244</td><td>0.484742743</td><td>0.34253893</td></tr>\n",
       "\t<tr><td>0.211019045</td><td>0.0191277507</td><td>0.14268224</td><td>0.738379088</td><td>0.259797501</td><td>0.036705005</td><td>0.0405133146</td><td>0.880904639</td><td>1.57433321</td><td>1.676201511</td><td>0.99560892</td></tr>\n",
       "\t<tr><td>0.003058325</td><td>0.4035754887</td><td>1.24332250</td><td>1.173098026</td><td>0.154845388</td><td>0.289326233</td><td>0.0535125856</td><td>0.962964536</td><td>0.81501399</td><td>2.449782834</td><td>1.67671611</td></tr>\n",
       "\t<tr><td>0.071754240</td><td>0.0506478109</td><td>3.20021011</td><td>0.391144240</td><td>0.394112839</td><td>0.352652919</td><td>0.0465491465</td><td>0.311449489</td><td>0.82025799</td><td>1.268758262</td><td>0.72074228</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 11 × 11\n",
       "\\begin{tabular}{lllllllllll}\n",
       " V1 & V2 & V3 & V4 & V5 & V6 & V7 & V8 & V9 & V10 & V11\\\\\n",
       " <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t 0.002850142 & 0.0051945133 & 0.17517252 & 0.105288975 & 0.000996861 & 0.019104863 & 0.0002870778 & 0.010257393 & 0.01416667 & 0.079881234 & 0.10041674\\\\\n",
       "\t 0.006409316 & 0.0284859626 & 0.10309967 & 0.210079567 & 0.020826692 & 0.015600300 & 0.0018662949 & 0.047081548 & 0.01783860 & 0.142668888 & 0.01081959\\\\\n",
       "\t 0.229680295 & 0.0688396307 & 1.17196394 & 0.146513956 & 0.280771854 & 0.467611933 & 0.0528214320 & 0.162583301 & 0.16079635 & 1.567725326 & 0.28700021\\\\\n",
       "\t 0.049674970 & 0.0735763430 & 0.39615750 & 0.436190619 & 0.126039669 & 0.037348363 & 0.0140674157 & 0.351904026 & 1.13230599 & 0.450782635 & 0.05586620\\\\\n",
       "\t 0.073742783 & 0.0485908592 & 0.94067023 & 0.812618964 & 0.205666668 & 0.117250151 & 0.0158285234 & 0.225690773 & 0.30863210 & 0.328418196 & 0.11238989\\\\\n",
       "\t 0.002083992 & 0.0086420844 & 0.07908055 & 0.076540781 & 0.016008070 & 0.006799890 & 0.0011081651 & 0.005092464 & 0.01379233 & 0.070763144 & 0.10809270\\\\\n",
       "\t 0.003343799 & 0.0002544001 & 0.01643626 & 0.007498777 & 0.001577697 & 0.001942791 & 0.0011556689 & 0.001239124 & 0.02428022 & 0.002966857 & 0.01762369\\\\\n",
       "\t 0.036816438 & 0.0001906352 & 0.65007164 & 0.394537236 & 0.031622788 & 0.021768776 & 0.0024036582 & 0.037203545 & 0.30538244 & 0.484742743 & 0.34253893\\\\\n",
       "\t 0.211019045 & 0.0191277507 & 0.14268224 & 0.738379088 & 0.259797501 & 0.036705005 & 0.0405133146 & 0.880904639 & 1.57433321 & 1.676201511 & 0.99560892\\\\\n",
       "\t 0.003058325 & 0.4035754887 & 1.24332250 & 1.173098026 & 0.154845388 & 0.289326233 & 0.0535125856 & 0.962964536 & 0.81501399 & 2.449782834 & 1.67671611\\\\\n",
       "\t 0.071754240 & 0.0506478109 & 3.20021011 & 0.391144240 & 0.394112839 & 0.352652919 & 0.0465491465 & 0.311449489 & 0.82025799 & 1.268758262 & 0.72074228\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 11 × 11\n",
       "\n",
       "| V1 &lt;dbl&gt; | V2 &lt;dbl&gt; | V3 &lt;dbl&gt; | V4 &lt;dbl&gt; | V5 &lt;dbl&gt; | V6 &lt;dbl&gt; | V7 &lt;dbl&gt; | V8 &lt;dbl&gt; | V9 &lt;dbl&gt; | V10 &lt;dbl&gt; | V11 &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 0.002850142 | 0.0051945133 | 0.17517252 | 0.105288975 | 0.000996861 | 0.019104863 | 0.0002870778 | 0.010257393 | 0.01416667 | 0.079881234 | 0.10041674 |\n",
       "| 0.006409316 | 0.0284859626 | 0.10309967 | 0.210079567 | 0.020826692 | 0.015600300 | 0.0018662949 | 0.047081548 | 0.01783860 | 0.142668888 | 0.01081959 |\n",
       "| 0.229680295 | 0.0688396307 | 1.17196394 | 0.146513956 | 0.280771854 | 0.467611933 | 0.0528214320 | 0.162583301 | 0.16079635 | 1.567725326 | 0.28700021 |\n",
       "| 0.049674970 | 0.0735763430 | 0.39615750 | 0.436190619 | 0.126039669 | 0.037348363 | 0.0140674157 | 0.351904026 | 1.13230599 | 0.450782635 | 0.05586620 |\n",
       "| 0.073742783 | 0.0485908592 | 0.94067023 | 0.812618964 | 0.205666668 | 0.117250151 | 0.0158285234 | 0.225690773 | 0.30863210 | 0.328418196 | 0.11238989 |\n",
       "| 0.002083992 | 0.0086420844 | 0.07908055 | 0.076540781 | 0.016008070 | 0.006799890 | 0.0011081651 | 0.005092464 | 0.01379233 | 0.070763144 | 0.10809270 |\n",
       "| 0.003343799 | 0.0002544001 | 0.01643626 | 0.007498777 | 0.001577697 | 0.001942791 | 0.0011556689 | 0.001239124 | 0.02428022 | 0.002966857 | 0.01762369 |\n",
       "| 0.036816438 | 0.0001906352 | 0.65007164 | 0.394537236 | 0.031622788 | 0.021768776 | 0.0024036582 | 0.037203545 | 0.30538244 | 0.484742743 | 0.34253893 |\n",
       "| 0.211019045 | 0.0191277507 | 0.14268224 | 0.738379088 | 0.259797501 | 0.036705005 | 0.0405133146 | 0.880904639 | 1.57433321 | 1.676201511 | 0.99560892 |\n",
       "| 0.003058325 | 0.4035754887 | 1.24332250 | 1.173098026 | 0.154845388 | 0.289326233 | 0.0535125856 | 0.962964536 | 0.81501399 | 2.449782834 | 1.67671611 |\n",
       "| 0.071754240 | 0.0506478109 | 3.20021011 | 0.391144240 | 0.394112839 | 0.352652919 | 0.0465491465 | 0.311449489 | 0.82025799 | 1.268758262 | 0.72074228 |\n",
       "\n"
      ],
      "text/plain": [
       "   V1          V2           V3         V4          V5          V6         \n",
       "1  0.002850142 0.0051945133 0.17517252 0.105288975 0.000996861 0.019104863\n",
       "2  0.006409316 0.0284859626 0.10309967 0.210079567 0.020826692 0.015600300\n",
       "3  0.229680295 0.0688396307 1.17196394 0.146513956 0.280771854 0.467611933\n",
       "4  0.049674970 0.0735763430 0.39615750 0.436190619 0.126039669 0.037348363\n",
       "5  0.073742783 0.0485908592 0.94067023 0.812618964 0.205666668 0.117250151\n",
       "6  0.002083992 0.0086420844 0.07908055 0.076540781 0.016008070 0.006799890\n",
       "7  0.003343799 0.0002544001 0.01643626 0.007498777 0.001577697 0.001942791\n",
       "8  0.036816438 0.0001906352 0.65007164 0.394537236 0.031622788 0.021768776\n",
       "9  0.211019045 0.0191277507 0.14268224 0.738379088 0.259797501 0.036705005\n",
       "10 0.003058325 0.4035754887 1.24332250 1.173098026 0.154845388 0.289326233\n",
       "11 0.071754240 0.0506478109 3.20021011 0.391144240 0.394112839 0.352652919\n",
       "   V7           V8          V9         V10         V11       \n",
       "1  0.0002870778 0.010257393 0.01416667 0.079881234 0.10041674\n",
       "2  0.0018662949 0.047081548 0.01783860 0.142668888 0.01081959\n",
       "3  0.0528214320 0.162583301 0.16079635 1.567725326 0.28700021\n",
       "4  0.0140674157 0.351904026 1.13230599 0.450782635 0.05586620\n",
       "5  0.0158285234 0.225690773 0.30863210 0.328418196 0.11238989\n",
       "6  0.0011081651 0.005092464 0.01379233 0.070763144 0.10809270\n",
       "7  0.0011556689 0.001239124 0.02428022 0.002966857 0.01762369\n",
       "8  0.0024036582 0.037203545 0.30538244 0.484742743 0.34253893\n",
       "9  0.0405133146 0.880904639 1.57433321 1.676201511 0.99560892\n",
       "10 0.0535125856 0.962964536 0.81501399 2.449782834 1.67671611\n",
       "11 0.0465491465 0.311449489 0.82025799 1.268758262 0.72074228"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#モデルの標準誤差\n",
    "SE <- model %>%\n",
    "  se %>%\n",
    "  apply(2, as.numeric) %>%\n",
    "  as.data.frame\n",
    "SE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "       V1                 V2                  V3                V4          \n",
       " Min.   :0.002084   Min.   :0.0001906   Min.   :0.01644   Min.   :0.007499  \n",
       " 1st Qu.:0.003201   1st Qu.:0.0069183   1st Qu.:0.12289   1st Qu.:0.125901  \n",
       " Median :0.036816   Median :0.0284860   Median :0.39616   Median :0.391144  \n",
       " Mean   :0.062767   Mean   :0.0642841   Mean   :0.73808   Mean   :0.408354  \n",
       " 3rd Qu.:0.072749   3rd Qu.:0.0597437   3rd Qu.:1.05632   3rd Qu.:0.587285  \n",
       " Max.   :0.229680   Max.   :0.4035755   Max.   :3.20021   Max.   :1.173098  \n",
       "       V5                  V6                 V7                  V8          \n",
       " Min.   :0.0009969   Min.   :0.001943   Min.   :0.0002871   Min.   :0.001239  \n",
       " 1st Qu.:0.0184174   1st Qu.:0.017353   1st Qu.:0.0015110   1st Qu.:0.023731  \n",
       " Median :0.1260397   Median :0.036705   Median :0.0140674   Median :0.162583  \n",
       " Mean   :0.1356605   Mean   :0.124192   Mean   :0.0209194   Mean   :0.272397  \n",
       " 3rd Qu.:0.2327321   3rd Qu.:0.203288   3rd Qu.:0.0435312   3rd Qu.:0.331677  \n",
       " Max.   :0.3941128   Max.   :0.467612   Max.   :0.0535126   Max.   :0.962965  \n",
       "       V9               V10                V11         \n",
       " Min.   :0.01379   Min.   :0.002967   Min.   :0.01082  \n",
       " 1st Qu.:0.02106   1st Qu.:0.111275   1st Qu.:0.07814  \n",
       " Median :0.30538   Median :0.450783   Median :0.11239  \n",
       " Mean   :0.47153   Mean   :0.774790   Mean   :0.40253  \n",
       " 3rd Qu.:0.81764   3rd Qu.:1.418242   3rd Qu.:0.53164  \n",
       " Max.   :1.57433   Max.   :2.449783   Max.   :1.67672  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#要約統計量を求める。\n",
    "SE %>%\n",
    "  summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 幾何ブラウン運動\n",
    "1. パラメータ設定\n",
    "1. 各列にシミュレートする。\n",
    "1. その確率ベクトルを横結合し、確率偏微分方程式を形成する。\n",
    "1. 結果の目視確認"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### パラメータ設定\n",
    "1. 標準偏回帰係数\n",
    "1. ドリフト項\n",
    "1. ボラティリティ\n",
    "1. シミュレーションの設定"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "#標準偏回帰係数\n",
    "SE_coefficient <- coefficient / SE %>%\n",
    "  apply(2, as.numeric) %>%\n",
    "  as.data.frame\n",
    "#ドリフト項\n",
    "mean_coefficient <- SE_coefficient %>%\n",
    "  apply(2, mean) %>%\n",
    "  as.data.frame\n",
    "#ボラティリティ\n",
    "volatility_coefficient <- SE_coefficient %>%\n",
    "  apply(2, sigma) %>%\n",
    "  as.data.frame\n",
    "#シミュレーションの設定\n",
    "N  <- 2050 - 1987 %>% as.numeric #標本数\n",
    "t0 <- 1987 %>% as.numeric #開始時(年)\n",
    "T  <- 2050 %>% as.numeric #終了時(年)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### パラメータ抽出\n",
    "1. ドリフト項\n",
    "1. ボラティリティ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "#ドリフト項\n",
    "mean_Y1  <- mean_coefficient[1, ]  %>% as.numeric\n",
    "mean_Y2  <- mean_coefficient[2, ]  %>% as.numeric\n",
    "mean_Y3  <- mean_coefficient[3, ]  %>% as.numeric\n",
    "mean_Y4  <- mean_coefficient[4, ]  %>% as.numeric\n",
    "mean_Y5  <- mean_coefficient[5, ]  %>% as.numeric\n",
    "mean_Y6  <- mean_coefficient[6, ]  %>% as.numeric\n",
    "mean_Y7  <- mean_coefficient[7, ]  %>% as.numeric\n",
    "mean_Y8  <- mean_coefficient[8, ]  %>% as.numeric\n",
    "mean_Y9  <- mean_coefficient[9, ]  %>% as.numeric\n",
    "mean_Y10 <- mean_coefficient[10, ] %>% as.numeric\n",
    "mean_Y11 <- mean_coefficient[11, ] %>% as.numeric\n",
    "#ボラティリティ\n",
    "volatility_Y1  <- volatility_coefficient[1, ]  %>% as.numeric\n",
    "volatility_Y2  <- volatility_coefficient[2, ]  %>% as.numeric\n",
    "volatility_Y3  <- volatility_coefficient[3, ]  %>% as.numeric\n",
    "volatility_Y4  <- volatility_coefficient[4, ]  %>% as.numeric\n",
    "volatility_Y5  <- volatility_coefficient[5, ]  %>% as.numeric\n",
    "volatility_Y6  <- volatility_coefficient[6, ]  %>% as.numeric\n",
    "volatility_Y7  <- volatility_coefficient[7, ]  %>% as.numeric\n",
    "volatility_Y8  <- volatility_coefficient[8, ]  %>% as.numeric\n",
    "volatility_Y9  <- volatility_coefficient[9, ]  %>% as.numeric\n",
    "volatility_Y10 <- volatility_coefficient[10, ] %>% as.numeric\n",
    "volatility_Y11 <- volatility_coefficient[11, ] %>% as.numeric"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 幾何ブラウン運動の実行\n",
    "- Y1~11について時間微分係数の期待値を求める。\n",
    "- 各列について実行する。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "#時間微分係数の期待値を求める。\n",
    "SDE_Y1  <- GBM(N = N, t0 = t0, theta = mean_Y1,  sigma = volatility_Y1,  T = T)  #Y1\n",
    "SDE_Y2  <- GBM(N = N, t0 = t0, theta = mean_Y2,  sigma = volatility_Y2,  T = T)  #Y2\n",
    "SDE_Y3  <- GBM(N = N, t0 = t0, theta = mean_Y3,  sigma = volatility_Y3,  T = T)  #Y3\n",
    "SDE_Y4  <- GBM(N = N, t0 = t0, theta = mean_Y4,  sigma = volatility_Y4,  T = T)  #Y4\n",
    "SDE_Y5  <- GBM(N = N, t0 = t0, theta = mean_Y5,  sigma = volatility_Y5,  T = T)  #Y5\n",
    "SDE_Y6  <- GBM(N = N, t0 = t0, theta = mean_Y6,  sigma = volatility_Y6,  T = T)  #Y6\n",
    "SDE_Y7  <- GBM(N = N, t0 = t0, theta = mean_Y7,  sigma = volatility_Y7,  T = T)  #Y7\n",
    "SDE_Y8  <- GBM(N = N, t0 = t0, theta = mean_Y8,  sigma = volatility_Y8,  T = T)  #Y8\n",
    "SDE_Y9  <- GBM(N = N, t0 = t0, theta = mean_Y9,  sigma = volatility_Y9,  T = T)  #Y9\n",
    "SDE_Y10 <- GBM(N = N, t0 = t0, theta = mean_Y10, sigma = volatility_Y10, T = T)  #Y10\n",
    "SDE_Y11 <- GBM(N = N, t0 = t0, theta = mean_Y11, sigma = volatility_Y11, T = T)  #Y11"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 確率偏微分方程式の形成\n",
    "1. 各列に実行した幾何ブラウン運動の結果を横結合する。\n",
    "1. 結果の目視確認を行う。\n",
    "1. 要約統計量を求める。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "New names:\n",
      "* NA -> ...1\n",
      "* NA -> ...2\n",
      "* NA -> ...3\n",
      "* NA -> ...4\n",
      "* NA -> ...5\n",
      "* ...\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 64 × 11</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Y1</th><th scope=col>Y2</th><th scope=col>Y3</th><th scope=col>Y4</th><th scope=col>Y5</th><th scope=col>Y6</th><th scope=col>Y7</th><th scope=col>Y8</th><th scope=col>Y9</th><th scope=col>Y10</th><th scope=col>Y11</th></tr>\n",
       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>0</td><td>1.215130e+142</td><td>0</td><td>0</td><td>2.793068e-295</td><td>2.793068e-295</td><td>2.793068e-295</td><td>0</td><td>6.836740e-96</td><td>2.793068e-295</td><td>0</td></tr>\n",
       "\t<tr><td>0</td><td>1.187310e+142</td><td>0</td><td>0</td><td>4.755710e-295</td><td>1.186560e-295</td><td>6.145261e-296</td><td>0</td><td>2.376106e-96</td><td>1.454210e-295</td><td>0</td></tr>\n",
       "\t<tr><td>0</td><td>7.805206e+141</td><td>0</td><td>0</td><td>2.024504e-295</td><td>6.700094e-296</td><td>8.676613e-296</td><td>0</td><td>3.951450e-96</td><td>1.237354e-295</td><td>0</td></tr>\n",
       "\t<tr><td>0</td><td>5.437949e+141</td><td>0</td><td>0</td><td>4.057125e-295</td><td>8.935600e-296</td><td>2.231381e-296</td><td>0</td><td>1.357407e-96</td><td>1.501785e-295</td><td>0</td></tr>\n",
       "\t<tr><td>0</td><td>5.480044e+141</td><td>0</td><td>0</td><td>1.874846e-295</td><td>1.721622e-295</td><td>1.043514e-296</td><td>0</td><td>1.063642e-96</td><td>4.692220e-295</td><td>0</td></tr>\n",
       "\t<tr><td>0</td><td>8.698185e+141</td><td>0</td><td>0</td><td>5.067863e-296</td><td>1.629863e-295</td><td>4.851646e-297</td><td>0</td><td>3.825619e-96</td><td>7.687726e-295</td><td>0</td></tr>\n",
       "\t<tr><td>0</td><td>8.656196e+141</td><td>0</td><td>0</td><td>6.451520e-296</td><td>1.492961e-295</td><td>1.084705e-297</td><td>0</td><td>1.309781e-96</td><td>5.052595e-295</td><td>0</td></tr>\n",
       "\t<tr><td>0</td><td>2.069204e+141</td><td>0</td><td>0</td><td>1.150784e-295</td><td>1.822083e-295</td><td>5.217276e-298</td><td>0</td><td>2.468681e-96</td><td>7.219396e-295</td><td>0</td></tr>\n",
       "\t<tr><td>0</td><td>6.100067e+141</td><td>0</td><td>0</td><td>1.506189e-295</td><td>2.091316e-295</td><td>1.172255e-297</td><td>0</td><td>2.745999e-96</td><td>1.632095e-294</td><td>0</td></tr>\n",
       "\t<tr><td>0</td><td>1.202963e+142</td><td>0</td><td>0</td><td>2.178270e-295</td><td>9.406781e-296</td><td>8.565935e-298</td><td>0</td><td>5.446001e-96</td><td>3.224698e-295</td><td>0</td></tr>\n",
       "\t<tr><td>0</td><td>1.118249e+142</td><td>0</td><td>0</td><td>4.776749e-296</td><td>2.901347e-295</td><td>3.783882e-298</td><td>0</td><td>6.361424e-96</td><td>2.045265e-295</td><td>0</td></tr>\n",
       "\t<tr><td>0</td><td>1.370099e+142</td><td>0</td><td>0</td><td>1.399276e-295</td><td>4.785086e-295</td><td>5.737635e-299</td><td>0</td><td>1.202364e-95</td><td>2.189736e-295</td><td>0</td></tr>\n",
       "\t<tr><td>0</td><td>8.823773e+141</td><td>0</td><td>0</td><td>2.592464e-296</td><td>2.004593e-294</td><td>2.867393e-298</td><td>0</td><td>3.032157e-95</td><td>8.122620e-296</td><td>0</td></tr>\n",
       "\t<tr><td>0</td><td>3.269629e+142</td><td>0</td><td>0</td><td>6.145476e-296</td><td>4.734512e-295</td><td>1.461304e-298</td><td>0</td><td>1.983787e-95</td><td>1.257280e-295</td><td>0</td></tr>\n",
       "\t<tr><td>0</td><td>7.669523e+142</td><td>0</td><td>0</td><td>9.816670e-297</td><td>9.326966e-295</td><td>3.853594e-299</td><td>0</td><td>4.231951e-95</td><td>4.323662e-296</td><td>0</td></tr>\n",
       "\t<tr><td>0</td><td>5.903525e+142</td><td>0</td><td>0</td><td>1.445472e-296</td><td>9.967019e-295</td><td>1.635429e-299</td><td>0</td><td>5.156641e-95</td><td>2.499823e-296</td><td>0</td></tr>\n",
       "\t<tr><td>0</td><td>4.390418e+142</td><td>0</td><td>0</td><td>5.744933e-297</td><td>1.500674e-294</td><td>2.686873e-299</td><td>0</td><td>1.178564e-95</td><td>8.681427e-296</td><td>0</td></tr>\n",
       "\t<tr><td>0</td><td>9.689287e+142</td><td>0</td><td>0</td><td>1.401983e-296</td><td>1.200373e-294</td><td>1.492392e-299</td><td>0</td><td>3.688941e-96</td><td>7.445303e-296</td><td>0</td></tr>\n",
       "\t<tr><td>0</td><td>2.847748e+143</td><td>0</td><td>0</td><td>9.318911e-297</td><td>3.779565e-295</td><td>1.556275e-299</td><td>0</td><td>1.562124e-96</td><td>6.970694e-296</td><td>0</td></tr>\n",
       "\t<tr><td>0</td><td>2.302062e+143</td><td>0</td><td>0</td><td>1.320577e-296</td><td>1.019241e-295</td><td>2.767060e-299</td><td>0</td><td>4.551410e-96</td><td>4.869890e-296</td><td>0</td></tr>\n",
       "\t<tr><td>0</td><td>1.702895e+143</td><td>0</td><td>0</td><td>1.555386e-296</td><td>1.271064e-296</td><td>1.440509e-299</td><td>0</td><td>3.139696e-96</td><td>5.573901e-296</td><td>0</td></tr>\n",
       "\t<tr><td>0</td><td>3.502352e+143</td><td>0</td><td>0</td><td>1.540693e-297</td><td>8.542344e-297</td><td>3.604824e-300</td><td>0</td><td>1.856132e-96</td><td>2.811784e-296</td><td>0</td></tr>\n",
       "\t<tr><td>0</td><td>2.447367e+143</td><td>0</td><td>0</td><td>3.094581e-298</td><td>9.547908e-297</td><td>1.965181e-300</td><td>0</td><td>7.184090e-97</td><td>6.390618e-297</td><td>0</td></tr>\n",
       "\t<tr><td>0</td><td>1.862198e+143</td><td>0</td><td>0</td><td>9.219635e-299</td><td>7.309931e-297</td><td>3.358475e-301</td><td>0</td><td>9.796218e-97</td><td>1.218714e-296</td><td>0</td></tr>\n",
       "\t<tr><td>0</td><td>3.295069e+143</td><td>0</td><td>0</td><td>2.925627e-299</td><td>4.620692e-297</td><td>2.744398e-301</td><td>0</td><td>9.533052e-97</td><td>2.002836e-297</td><td>0</td></tr>\n",
       "\t<tr><td>0</td><td>5.870060e+143</td><td>0</td><td>0</td><td>1.043499e-298</td><td>1.687481e-297</td><td>3.574772e-302</td><td>0</td><td>1.543463e-96</td><td>3.264663e-297</td><td>0</td></tr>\n",
       "\t<tr><td>0</td><td>9.519732e+143</td><td>0</td><td>0</td><td>1.405758e-299</td><td>2.525146e-299</td><td>1.714314e-302</td><td>0</td><td>2.059532e-96</td><td>2.991420e-297</td><td>0</td></tr>\n",
       "\t<tr><td>0</td><td>9.898609e+143</td><td>0</td><td>0</td><td>6.565097e-299</td><td>6.744314e-300</td><td>1.260531e-302</td><td>0</td><td>1.055902e-96</td><td>2.237489e-297</td><td>0</td></tr>\n",
       "\t<tr><td>0</td><td>1.849046e+143</td><td>0</td><td>0</td><td>3.917448e-299</td><td>2.064595e-300</td><td>1.269698e-302</td><td>0</td><td>1.060601e-96</td><td>5.874670e-298</td><td>0</td></tr>\n",
       "\t<tr><td>0</td><td>7.345389e+142</td><td>0</td><td>0</td><td>1.580585e-299</td><td>5.826607e-301</td><td>3.368440e-303</td><td>0</td><td>3.578282e-96</td><td>3.834965e-298</td><td>0</td></tr>\n",
       "\t<tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>\n",
       "\t<tr><td>0</td><td>1.925858e+143</td><td>0</td><td>0</td><td>1.407720e-299</td><td>5.116083e-300</td><td>8.077541e-304</td><td>0</td><td> 3.066516e-96</td><td>2.294926e-298</td><td>0</td></tr>\n",
       "\t<tr><td>0</td><td>8.668908e+143</td><td>0</td><td>0</td><td>8.275344e-299</td><td>5.416940e-301</td><td>2.202424e-304</td><td>0</td><td> 4.313420e-97</td><td>7.755364e-299</td><td>0</td></tr>\n",
       "\t<tr><td>0</td><td>3.405284e+143</td><td>0</td><td>0</td><td>4.056502e-299</td><td>5.915549e-301</td><td>3.673498e-304</td><td>0</td><td> 4.683306e-97</td><td>3.583295e-299</td><td>0</td></tr>\n",
       "\t<tr><td>0</td><td>5.145513e+143</td><td>0</td><td>0</td><td>3.850370e-299</td><td>4.049806e-301</td><td>3.138667e-304</td><td>0</td><td> 1.859243e-96</td><td>4.701828e-300</td><td>0</td></tr>\n",
       "\t<tr><td>0</td><td>3.276732e+143</td><td>0</td><td>0</td><td>2.217683e-299</td><td>1.791698e-301</td><td>4.675894e-304</td><td>0</td><td> 1.210274e-96</td><td>2.884976e-300</td><td>0</td></tr>\n",
       "\t<tr><td>0</td><td>6.336441e+143</td><td>0</td><td>0</td><td>9.508096e-300</td><td>4.299340e-301</td><td>7.366875e-304</td><td>0</td><td> 4.985211e-97</td><td>7.157361e-301</td><td>0</td></tr>\n",
       "\t<tr><td>0</td><td>4.293976e+143</td><td>0</td><td>0</td><td>1.821631e-299</td><td>5.272119e-301</td><td>2.396659e-304</td><td>0</td><td> 1.149113e-97</td><td>5.679973e-301</td><td>0</td></tr>\n",
       "\t<tr><td>0</td><td>3.346470e+143</td><td>0</td><td>0</td><td>7.487404e-300</td><td>2.001042e-301</td><td>3.839237e-305</td><td>0</td><td> 7.930051e-98</td><td>1.121549e-301</td><td>0</td></tr>\n",
       "\t<tr><td>0</td><td>3.191134e+143</td><td>0</td><td>0</td><td>4.382293e-300</td><td>3.895514e-301</td><td>2.610819e-305</td><td>0</td><td> 5.399128e-98</td><td>9.849269e-303</td><td>0</td></tr>\n",
       "\t<tr><td>0</td><td>3.967514e+143</td><td>0</td><td>0</td><td>1.486236e-300</td><td>1.016371e-301</td><td>2.855538e-305</td><td>0</td><td> 6.792340e-99</td><td>6.578958e-303</td><td>0</td></tr>\n",
       "\t<tr><td>0</td><td>2.166426e+143</td><td>0</td><td>0</td><td>9.035239e-301</td><td>9.469662e-302</td><td>3.108179e-306</td><td>0</td><td> 4.420937e-99</td><td>1.596134e-302</td><td>0</td></tr>\n",
       "\t<tr><td>0</td><td>4.519078e+143</td><td>0</td><td>0</td><td>1.522575e-301</td><td>1.779810e-301</td><td>1.813840e-306</td><td>0</td><td> 4.454108e-99</td><td>1.100628e-302</td><td>0</td></tr>\n",
       "\t<tr><td>0</td><td>1.225237e+144</td><td>0</td><td>0</td><td>3.026662e-302</td><td>5.410157e-302</td><td>6.112602e-307</td><td>0</td><td>7.794922e-100</td><td>1.046148e-302</td><td>0</td></tr>\n",
       "\t<tr><td>0</td><td>6.749257e+143</td><td>0</td><td>0</td><td>9.810307e-302</td><td>5.765540e-302</td><td>2.669472e-307</td><td>0</td><td> 1.640562e-99</td><td>8.507125e-303</td><td>0</td></tr>\n",
       "\t<tr><td>0</td><td>5.266472e+143</td><td>0</td><td>0</td><td>6.595727e-302</td><td>4.540645e-302</td><td>7.239643e-308</td><td>0</td><td>8.924392e-101</td><td>1.555921e-302</td><td>0</td></tr>\n",
       "\t<tr><td>0</td><td>7.866628e+143</td><td>0</td><td>0</td><td>1.906635e-302</td><td>5.368990e-302</td><td>5.597210e-308</td><td>0</td><td>2.536269e-100</td><td>1.368508e-302</td><td>0</td></tr>\n",
       "\t<tr><td>0</td><td>1.255178e+144</td><td>0</td><td>0</td><td>7.888714e-303</td><td>5.276246e-302</td><td>1.483093e-308</td><td>0</td><td>4.843548e-100</td><td>1.614362e-302</td><td>0</td></tr>\n",
       "\t<tr><td>0</td><td>1.056501e+144</td><td>0</td><td>0</td><td>1.463815e-303</td><td>3.801940e-302</td><td>5.487722e-308</td><td>0</td><td> 1.355931e-99</td><td>2.639777e-302</td><td>0</td></tr>\n",
       "\t<tr><td>0</td><td>2.171380e+144</td><td>0</td><td>0</td><td>2.997213e-303</td><td>3.856706e-302</td><td>4.862166e-308</td><td>0</td><td> 4.335400e-99</td><td>1.306470e-302</td><td>0</td></tr>\n",
       "\t<tr><td>0</td><td>3.444916e+144</td><td>0</td><td>0</td><td>3.222713e-303</td><td>2.173719e-302</td><td>5.441804e-308</td><td>0</td><td> 4.385336e-99</td><td>1.093714e-302</td><td>0</td></tr>\n",
       "\t<tr><td>0</td><td>3.277033e+144</td><td>0</td><td>0</td><td>8.946544e-303</td><td>7.381324e-303</td><td>3.831026e-308</td><td>0</td><td> 4.237025e-99</td><td>2.870760e-302</td><td>0</td></tr>\n",
       "\t<tr><td>0</td><td>2.375256e+144</td><td>0</td><td>0</td><td>1.140013e-302</td><td>7.352856e-303</td><td>3.616415e-308</td><td>0</td><td> 4.599892e-99</td><td>1.012123e-302</td><td>0</td></tr>\n",
       "\t<tr><td>0</td><td>2.217816e+144</td><td>0</td><td>0</td><td>5.229429e-303</td><td>1.858480e-303</td><td>2.953806e-308</td><td>0</td><td> 6.855735e-99</td><td>3.284830e-302</td><td>0</td></tr>\n",
       "\t<tr><td>0</td><td>7.813813e+143</td><td>0</td><td>0</td><td>2.479188e-302</td><td>2.253427e-303</td><td>2.908792e-308</td><td>0</td><td> 4.284093e-98</td><td>7.682802e-302</td><td>0</td></tr>\n",
       "\t<tr><td>0</td><td>8.154483e+143</td><td>0</td><td>0</td><td>1.510394e-302</td><td>1.253921e-303</td><td>3.083610e-308</td><td>0</td><td> 3.655518e-98</td><td>1.248841e-301</td><td>0</td></tr>\n",
       "\t<tr><td>0</td><td>6.155162e+143</td><td>0</td><td>0</td><td>9.708519e-303</td><td>1.256789e-303</td><td>2.998998e-308</td><td>0</td><td> 8.630025e-99</td><td>5.137041e-302</td><td>0</td></tr>\n",
       "\t<tr><td>0</td><td>5.717270e+143</td><td>0</td><td>0</td><td>1.364811e-302</td><td>3.618841e-304</td><td>4.259334e-308</td><td>0</td><td> 9.844238e-99</td><td>1.044006e-302</td><td>0</td></tr>\n",
       "\t<tr><td>0</td><td>1.325654e+144</td><td>0</td><td>0</td><td>1.159019e-302</td><td>3.555243e-304</td><td>2.116205e-308</td><td>0</td><td> 8.980722e-99</td><td>5.469330e-303</td><td>0</td></tr>\n",
       "\t<tr><td>0</td><td>3.187082e+143</td><td>0</td><td>0</td><td>1.126228e-302</td><td>4.138938e-304</td><td>4.546589e-308</td><td>0</td><td> 1.142863e-98</td><td>1.949048e-303</td><td>0</td></tr>\n",
       "\t<tr><td>0</td><td>1.382433e+143</td><td>0</td><td>0</td><td>5.523864e-304</td><td>1.976697e-304</td><td>3.266907e-307</td><td>0</td><td> 3.173911e-99</td><td>6.340404e-304</td><td>0</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 64 × 11\n",
       "\\begin{tabular}{lllllllllll}\n",
       " Y1 & Y2 & Y3 & Y4 & Y5 & Y6 & Y7 & Y8 & Y9 & Y10 & Y11\\\\\n",
       " <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t 0 & 1.215130e+142 & 0 & 0 & 2.793068e-295 & 2.793068e-295 & 2.793068e-295 & 0 & 6.836740e-96 & 2.793068e-295 & 0\\\\\n",
       "\t 0 & 1.187310e+142 & 0 & 0 & 4.755710e-295 & 1.186560e-295 & 6.145261e-296 & 0 & 2.376106e-96 & 1.454210e-295 & 0\\\\\n",
       "\t 0 & 7.805206e+141 & 0 & 0 & 2.024504e-295 & 6.700094e-296 & 8.676613e-296 & 0 & 3.951450e-96 & 1.237354e-295 & 0\\\\\n",
       "\t 0 & 5.437949e+141 & 0 & 0 & 4.057125e-295 & 8.935600e-296 & 2.231381e-296 & 0 & 1.357407e-96 & 1.501785e-295 & 0\\\\\n",
       "\t 0 & 5.480044e+141 & 0 & 0 & 1.874846e-295 & 1.721622e-295 & 1.043514e-296 & 0 & 1.063642e-96 & 4.692220e-295 & 0\\\\\n",
       "\t 0 & 8.698185e+141 & 0 & 0 & 5.067863e-296 & 1.629863e-295 & 4.851646e-297 & 0 & 3.825619e-96 & 7.687726e-295 & 0\\\\\n",
       "\t 0 & 8.656196e+141 & 0 & 0 & 6.451520e-296 & 1.492961e-295 & 1.084705e-297 & 0 & 1.309781e-96 & 5.052595e-295 & 0\\\\\n",
       "\t 0 & 2.069204e+141 & 0 & 0 & 1.150784e-295 & 1.822083e-295 & 5.217276e-298 & 0 & 2.468681e-96 & 7.219396e-295 & 0\\\\\n",
       "\t 0 & 6.100067e+141 & 0 & 0 & 1.506189e-295 & 2.091316e-295 & 1.172255e-297 & 0 & 2.745999e-96 & 1.632095e-294 & 0\\\\\n",
       "\t 0 & 1.202963e+142 & 0 & 0 & 2.178270e-295 & 9.406781e-296 & 8.565935e-298 & 0 & 5.446001e-96 & 3.224698e-295 & 0\\\\\n",
       "\t 0 & 1.118249e+142 & 0 & 0 & 4.776749e-296 & 2.901347e-295 & 3.783882e-298 & 0 & 6.361424e-96 & 2.045265e-295 & 0\\\\\n",
       "\t 0 & 1.370099e+142 & 0 & 0 & 1.399276e-295 & 4.785086e-295 & 5.737635e-299 & 0 & 1.202364e-95 & 2.189736e-295 & 0\\\\\n",
       "\t 0 & 8.823773e+141 & 0 & 0 & 2.592464e-296 & 2.004593e-294 & 2.867393e-298 & 0 & 3.032157e-95 & 8.122620e-296 & 0\\\\\n",
       "\t 0 & 3.269629e+142 & 0 & 0 & 6.145476e-296 & 4.734512e-295 & 1.461304e-298 & 0 & 1.983787e-95 & 1.257280e-295 & 0\\\\\n",
       "\t 0 & 7.669523e+142 & 0 & 0 & 9.816670e-297 & 9.326966e-295 & 3.853594e-299 & 0 & 4.231951e-95 & 4.323662e-296 & 0\\\\\n",
       "\t 0 & 5.903525e+142 & 0 & 0 & 1.445472e-296 & 9.967019e-295 & 1.635429e-299 & 0 & 5.156641e-95 & 2.499823e-296 & 0\\\\\n",
       "\t 0 & 4.390418e+142 & 0 & 0 & 5.744933e-297 & 1.500674e-294 & 2.686873e-299 & 0 & 1.178564e-95 & 8.681427e-296 & 0\\\\\n",
       "\t 0 & 9.689287e+142 & 0 & 0 & 1.401983e-296 & 1.200373e-294 & 1.492392e-299 & 0 & 3.688941e-96 & 7.445303e-296 & 0\\\\\n",
       "\t 0 & 2.847748e+143 & 0 & 0 & 9.318911e-297 & 3.779565e-295 & 1.556275e-299 & 0 & 1.562124e-96 & 6.970694e-296 & 0\\\\\n",
       "\t 0 & 2.302062e+143 & 0 & 0 & 1.320577e-296 & 1.019241e-295 & 2.767060e-299 & 0 & 4.551410e-96 & 4.869890e-296 & 0\\\\\n",
       "\t 0 & 1.702895e+143 & 0 & 0 & 1.555386e-296 & 1.271064e-296 & 1.440509e-299 & 0 & 3.139696e-96 & 5.573901e-296 & 0\\\\\n",
       "\t 0 & 3.502352e+143 & 0 & 0 & 1.540693e-297 & 8.542344e-297 & 3.604824e-300 & 0 & 1.856132e-96 & 2.811784e-296 & 0\\\\\n",
       "\t 0 & 2.447367e+143 & 0 & 0 & 3.094581e-298 & 9.547908e-297 & 1.965181e-300 & 0 & 7.184090e-97 & 6.390618e-297 & 0\\\\\n",
       "\t 0 & 1.862198e+143 & 0 & 0 & 9.219635e-299 & 7.309931e-297 & 3.358475e-301 & 0 & 9.796218e-97 & 1.218714e-296 & 0\\\\\n",
       "\t 0 & 3.295069e+143 & 0 & 0 & 2.925627e-299 & 4.620692e-297 & 2.744398e-301 & 0 & 9.533052e-97 & 2.002836e-297 & 0\\\\\n",
       "\t 0 & 5.870060e+143 & 0 & 0 & 1.043499e-298 & 1.687481e-297 & 3.574772e-302 & 0 & 1.543463e-96 & 3.264663e-297 & 0\\\\\n",
       "\t 0 & 9.519732e+143 & 0 & 0 & 1.405758e-299 & 2.525146e-299 & 1.714314e-302 & 0 & 2.059532e-96 & 2.991420e-297 & 0\\\\\n",
       "\t 0 & 9.898609e+143 & 0 & 0 & 6.565097e-299 & 6.744314e-300 & 1.260531e-302 & 0 & 1.055902e-96 & 2.237489e-297 & 0\\\\\n",
       "\t 0 & 1.849046e+143 & 0 & 0 & 3.917448e-299 & 2.064595e-300 & 1.269698e-302 & 0 & 1.060601e-96 & 5.874670e-298 & 0\\\\\n",
       "\t 0 & 7.345389e+142 & 0 & 0 & 1.580585e-299 & 5.826607e-301 & 3.368440e-303 & 0 & 3.578282e-96 & 3.834965e-298 & 0\\\\\n",
       "\t ... & ... & ... & ... & ... & ... & ... & ... & ... & ... & ...\\\\\n",
       "\t 0 & 1.925858e+143 & 0 & 0 & 1.407720e-299 & 5.116083e-300 & 8.077541e-304 & 0 &  3.066516e-96 & 2.294926e-298 & 0\\\\\n",
       "\t 0 & 8.668908e+143 & 0 & 0 & 8.275344e-299 & 5.416940e-301 & 2.202424e-304 & 0 &  4.313420e-97 & 7.755364e-299 & 0\\\\\n",
       "\t 0 & 3.405284e+143 & 0 & 0 & 4.056502e-299 & 5.915549e-301 & 3.673498e-304 & 0 &  4.683306e-97 & 3.583295e-299 & 0\\\\\n",
       "\t 0 & 5.145513e+143 & 0 & 0 & 3.850370e-299 & 4.049806e-301 & 3.138667e-304 & 0 &  1.859243e-96 & 4.701828e-300 & 0\\\\\n",
       "\t 0 & 3.276732e+143 & 0 & 0 & 2.217683e-299 & 1.791698e-301 & 4.675894e-304 & 0 &  1.210274e-96 & 2.884976e-300 & 0\\\\\n",
       "\t 0 & 6.336441e+143 & 0 & 0 & 9.508096e-300 & 4.299340e-301 & 7.366875e-304 & 0 &  4.985211e-97 & 7.157361e-301 & 0\\\\\n",
       "\t 0 & 4.293976e+143 & 0 & 0 & 1.821631e-299 & 5.272119e-301 & 2.396659e-304 & 0 &  1.149113e-97 & 5.679973e-301 & 0\\\\\n",
       "\t 0 & 3.346470e+143 & 0 & 0 & 7.487404e-300 & 2.001042e-301 & 3.839237e-305 & 0 &  7.930051e-98 & 1.121549e-301 & 0\\\\\n",
       "\t 0 & 3.191134e+143 & 0 & 0 & 4.382293e-300 & 3.895514e-301 & 2.610819e-305 & 0 &  5.399128e-98 & 9.849269e-303 & 0\\\\\n",
       "\t 0 & 3.967514e+143 & 0 & 0 & 1.486236e-300 & 1.016371e-301 & 2.855538e-305 & 0 &  6.792340e-99 & 6.578958e-303 & 0\\\\\n",
       "\t 0 & 2.166426e+143 & 0 & 0 & 9.035239e-301 & 9.469662e-302 & 3.108179e-306 & 0 &  4.420937e-99 & 1.596134e-302 & 0\\\\\n",
       "\t 0 & 4.519078e+143 & 0 & 0 & 1.522575e-301 & 1.779810e-301 & 1.813840e-306 & 0 &  4.454108e-99 & 1.100628e-302 & 0\\\\\n",
       "\t 0 & 1.225237e+144 & 0 & 0 & 3.026662e-302 & 5.410157e-302 & 6.112602e-307 & 0 & 7.794922e-100 & 1.046148e-302 & 0\\\\\n",
       "\t 0 & 6.749257e+143 & 0 & 0 & 9.810307e-302 & 5.765540e-302 & 2.669472e-307 & 0 &  1.640562e-99 & 8.507125e-303 & 0\\\\\n",
       "\t 0 & 5.266472e+143 & 0 & 0 & 6.595727e-302 & 4.540645e-302 & 7.239643e-308 & 0 & 8.924392e-101 & 1.555921e-302 & 0\\\\\n",
       "\t 0 & 7.866628e+143 & 0 & 0 & 1.906635e-302 & 5.368990e-302 & 5.597210e-308 & 0 & 2.536269e-100 & 1.368508e-302 & 0\\\\\n",
       "\t 0 & 1.255178e+144 & 0 & 0 & 7.888714e-303 & 5.276246e-302 & 1.483093e-308 & 0 & 4.843548e-100 & 1.614362e-302 & 0\\\\\n",
       "\t 0 & 1.056501e+144 & 0 & 0 & 1.463815e-303 & 3.801940e-302 & 5.487722e-308 & 0 &  1.355931e-99 & 2.639777e-302 & 0\\\\\n",
       "\t 0 & 2.171380e+144 & 0 & 0 & 2.997213e-303 & 3.856706e-302 & 4.862166e-308 & 0 &  4.335400e-99 & 1.306470e-302 & 0\\\\\n",
       "\t 0 & 3.444916e+144 & 0 & 0 & 3.222713e-303 & 2.173719e-302 & 5.441804e-308 & 0 &  4.385336e-99 & 1.093714e-302 & 0\\\\\n",
       "\t 0 & 3.277033e+144 & 0 & 0 & 8.946544e-303 & 7.381324e-303 & 3.831026e-308 & 0 &  4.237025e-99 & 2.870760e-302 & 0\\\\\n",
       "\t 0 & 2.375256e+144 & 0 & 0 & 1.140013e-302 & 7.352856e-303 & 3.616415e-308 & 0 &  4.599892e-99 & 1.012123e-302 & 0\\\\\n",
       "\t 0 & 2.217816e+144 & 0 & 0 & 5.229429e-303 & 1.858480e-303 & 2.953806e-308 & 0 &  6.855735e-99 & 3.284830e-302 & 0\\\\\n",
       "\t 0 & 7.813813e+143 & 0 & 0 & 2.479188e-302 & 2.253427e-303 & 2.908792e-308 & 0 &  4.284093e-98 & 7.682802e-302 & 0\\\\\n",
       "\t 0 & 8.154483e+143 & 0 & 0 & 1.510394e-302 & 1.253921e-303 & 3.083610e-308 & 0 &  3.655518e-98 & 1.248841e-301 & 0\\\\\n",
       "\t 0 & 6.155162e+143 & 0 & 0 & 9.708519e-303 & 1.256789e-303 & 2.998998e-308 & 0 &  8.630025e-99 & 5.137041e-302 & 0\\\\\n",
       "\t 0 & 5.717270e+143 & 0 & 0 & 1.364811e-302 & 3.618841e-304 & 4.259334e-308 & 0 &  9.844238e-99 & 1.044006e-302 & 0\\\\\n",
       "\t 0 & 1.325654e+144 & 0 & 0 & 1.159019e-302 & 3.555243e-304 & 2.116205e-308 & 0 &  8.980722e-99 & 5.469330e-303 & 0\\\\\n",
       "\t 0 & 3.187082e+143 & 0 & 0 & 1.126228e-302 & 4.138938e-304 & 4.546589e-308 & 0 &  1.142863e-98 & 1.949048e-303 & 0\\\\\n",
       "\t 0 & 1.382433e+143 & 0 & 0 & 5.523864e-304 & 1.976697e-304 & 3.266907e-307 & 0 &  3.173911e-99 & 6.340404e-304 & 0\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 64 × 11\n",
       "\n",
       "| Y1 &lt;dbl&gt; | Y2 &lt;dbl&gt; | Y3 &lt;dbl&gt; | Y4 &lt;dbl&gt; | Y5 &lt;dbl&gt; | Y6 &lt;dbl&gt; | Y7 &lt;dbl&gt; | Y8 &lt;dbl&gt; | Y9 &lt;dbl&gt; | Y10 &lt;dbl&gt; | Y11 &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 0 | 1.215130e+142 | 0 | 0 | 2.793068e-295 | 2.793068e-295 | 2.793068e-295 | 0 | 6.836740e-96 | 2.793068e-295 | 0 |\n",
       "| 0 | 1.187310e+142 | 0 | 0 | 4.755710e-295 | 1.186560e-295 | 6.145261e-296 | 0 | 2.376106e-96 | 1.454210e-295 | 0 |\n",
       "| 0 | 7.805206e+141 | 0 | 0 | 2.024504e-295 | 6.700094e-296 | 8.676613e-296 | 0 | 3.951450e-96 | 1.237354e-295 | 0 |\n",
       "| 0 | 5.437949e+141 | 0 | 0 | 4.057125e-295 | 8.935600e-296 | 2.231381e-296 | 0 | 1.357407e-96 | 1.501785e-295 | 0 |\n",
       "| 0 | 5.480044e+141 | 0 | 0 | 1.874846e-295 | 1.721622e-295 | 1.043514e-296 | 0 | 1.063642e-96 | 4.692220e-295 | 0 |\n",
       "| 0 | 8.698185e+141 | 0 | 0 | 5.067863e-296 | 1.629863e-295 | 4.851646e-297 | 0 | 3.825619e-96 | 7.687726e-295 | 0 |\n",
       "| 0 | 8.656196e+141 | 0 | 0 | 6.451520e-296 | 1.492961e-295 | 1.084705e-297 | 0 | 1.309781e-96 | 5.052595e-295 | 0 |\n",
       "| 0 | 2.069204e+141 | 0 | 0 | 1.150784e-295 | 1.822083e-295 | 5.217276e-298 | 0 | 2.468681e-96 | 7.219396e-295 | 0 |\n",
       "| 0 | 6.100067e+141 | 0 | 0 | 1.506189e-295 | 2.091316e-295 | 1.172255e-297 | 0 | 2.745999e-96 | 1.632095e-294 | 0 |\n",
       "| 0 | 1.202963e+142 | 0 | 0 | 2.178270e-295 | 9.406781e-296 | 8.565935e-298 | 0 | 5.446001e-96 | 3.224698e-295 | 0 |\n",
       "| 0 | 1.118249e+142 | 0 | 0 | 4.776749e-296 | 2.901347e-295 | 3.783882e-298 | 0 | 6.361424e-96 | 2.045265e-295 | 0 |\n",
       "| 0 | 1.370099e+142 | 0 | 0 | 1.399276e-295 | 4.785086e-295 | 5.737635e-299 | 0 | 1.202364e-95 | 2.189736e-295 | 0 |\n",
       "| 0 | 8.823773e+141 | 0 | 0 | 2.592464e-296 | 2.004593e-294 | 2.867393e-298 | 0 | 3.032157e-95 | 8.122620e-296 | 0 |\n",
       "| 0 | 3.269629e+142 | 0 | 0 | 6.145476e-296 | 4.734512e-295 | 1.461304e-298 | 0 | 1.983787e-95 | 1.257280e-295 | 0 |\n",
       "| 0 | 7.669523e+142 | 0 | 0 | 9.816670e-297 | 9.326966e-295 | 3.853594e-299 | 0 | 4.231951e-95 | 4.323662e-296 | 0 |\n",
       "| 0 | 5.903525e+142 | 0 | 0 | 1.445472e-296 | 9.967019e-295 | 1.635429e-299 | 0 | 5.156641e-95 | 2.499823e-296 | 0 |\n",
       "| 0 | 4.390418e+142 | 0 | 0 | 5.744933e-297 | 1.500674e-294 | 2.686873e-299 | 0 | 1.178564e-95 | 8.681427e-296 | 0 |\n",
       "| 0 | 9.689287e+142 | 0 | 0 | 1.401983e-296 | 1.200373e-294 | 1.492392e-299 | 0 | 3.688941e-96 | 7.445303e-296 | 0 |\n",
       "| 0 | 2.847748e+143 | 0 | 0 | 9.318911e-297 | 3.779565e-295 | 1.556275e-299 | 0 | 1.562124e-96 | 6.970694e-296 | 0 |\n",
       "| 0 | 2.302062e+143 | 0 | 0 | 1.320577e-296 | 1.019241e-295 | 2.767060e-299 | 0 | 4.551410e-96 | 4.869890e-296 | 0 |\n",
       "| 0 | 1.702895e+143 | 0 | 0 | 1.555386e-296 | 1.271064e-296 | 1.440509e-299 | 0 | 3.139696e-96 | 5.573901e-296 | 0 |\n",
       "| 0 | 3.502352e+143 | 0 | 0 | 1.540693e-297 | 8.542344e-297 | 3.604824e-300 | 0 | 1.856132e-96 | 2.811784e-296 | 0 |\n",
       "| 0 | 2.447367e+143 | 0 | 0 | 3.094581e-298 | 9.547908e-297 | 1.965181e-300 | 0 | 7.184090e-97 | 6.390618e-297 | 0 |\n",
       "| 0 | 1.862198e+143 | 0 | 0 | 9.219635e-299 | 7.309931e-297 | 3.358475e-301 | 0 | 9.796218e-97 | 1.218714e-296 | 0 |\n",
       "| 0 | 3.295069e+143 | 0 | 0 | 2.925627e-299 | 4.620692e-297 | 2.744398e-301 | 0 | 9.533052e-97 | 2.002836e-297 | 0 |\n",
       "| 0 | 5.870060e+143 | 0 | 0 | 1.043499e-298 | 1.687481e-297 | 3.574772e-302 | 0 | 1.543463e-96 | 3.264663e-297 | 0 |\n",
       "| 0 | 9.519732e+143 | 0 | 0 | 1.405758e-299 | 2.525146e-299 | 1.714314e-302 | 0 | 2.059532e-96 | 2.991420e-297 | 0 |\n",
       "| 0 | 9.898609e+143 | 0 | 0 | 6.565097e-299 | 6.744314e-300 | 1.260531e-302 | 0 | 1.055902e-96 | 2.237489e-297 | 0 |\n",
       "| 0 | 1.849046e+143 | 0 | 0 | 3.917448e-299 | 2.064595e-300 | 1.269698e-302 | 0 | 1.060601e-96 | 5.874670e-298 | 0 |\n",
       "| 0 | 7.345389e+142 | 0 | 0 | 1.580585e-299 | 5.826607e-301 | 3.368440e-303 | 0 | 3.578282e-96 | 3.834965e-298 | 0 |\n",
       "| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |\n",
       "| 0 | 1.925858e+143 | 0 | 0 | 1.407720e-299 | 5.116083e-300 | 8.077541e-304 | 0 |  3.066516e-96 | 2.294926e-298 | 0 |\n",
       "| 0 | 8.668908e+143 | 0 | 0 | 8.275344e-299 | 5.416940e-301 | 2.202424e-304 | 0 |  4.313420e-97 | 7.755364e-299 | 0 |\n",
       "| 0 | 3.405284e+143 | 0 | 0 | 4.056502e-299 | 5.915549e-301 | 3.673498e-304 | 0 |  4.683306e-97 | 3.583295e-299 | 0 |\n",
       "| 0 | 5.145513e+143 | 0 | 0 | 3.850370e-299 | 4.049806e-301 | 3.138667e-304 | 0 |  1.859243e-96 | 4.701828e-300 | 0 |\n",
       "| 0 | 3.276732e+143 | 0 | 0 | 2.217683e-299 | 1.791698e-301 | 4.675894e-304 | 0 |  1.210274e-96 | 2.884976e-300 | 0 |\n",
       "| 0 | 6.336441e+143 | 0 | 0 | 9.508096e-300 | 4.299340e-301 | 7.366875e-304 | 0 |  4.985211e-97 | 7.157361e-301 | 0 |\n",
       "| 0 | 4.293976e+143 | 0 | 0 | 1.821631e-299 | 5.272119e-301 | 2.396659e-304 | 0 |  1.149113e-97 | 5.679973e-301 | 0 |\n",
       "| 0 | 3.346470e+143 | 0 | 0 | 7.487404e-300 | 2.001042e-301 | 3.839237e-305 | 0 |  7.930051e-98 | 1.121549e-301 | 0 |\n",
       "| 0 | 3.191134e+143 | 0 | 0 | 4.382293e-300 | 3.895514e-301 | 2.610819e-305 | 0 |  5.399128e-98 | 9.849269e-303 | 0 |\n",
       "| 0 | 3.967514e+143 | 0 | 0 | 1.486236e-300 | 1.016371e-301 | 2.855538e-305 | 0 |  6.792340e-99 | 6.578958e-303 | 0 |\n",
       "| 0 | 2.166426e+143 | 0 | 0 | 9.035239e-301 | 9.469662e-302 | 3.108179e-306 | 0 |  4.420937e-99 | 1.596134e-302 | 0 |\n",
       "| 0 | 4.519078e+143 | 0 | 0 | 1.522575e-301 | 1.779810e-301 | 1.813840e-306 | 0 |  4.454108e-99 | 1.100628e-302 | 0 |\n",
       "| 0 | 1.225237e+144 | 0 | 0 | 3.026662e-302 | 5.410157e-302 | 6.112602e-307 | 0 | 7.794922e-100 | 1.046148e-302 | 0 |\n",
       "| 0 | 6.749257e+143 | 0 | 0 | 9.810307e-302 | 5.765540e-302 | 2.669472e-307 | 0 |  1.640562e-99 | 8.507125e-303 | 0 |\n",
       "| 0 | 5.266472e+143 | 0 | 0 | 6.595727e-302 | 4.540645e-302 | 7.239643e-308 | 0 | 8.924392e-101 | 1.555921e-302 | 0 |\n",
       "| 0 | 7.866628e+143 | 0 | 0 | 1.906635e-302 | 5.368990e-302 | 5.597210e-308 | 0 | 2.536269e-100 | 1.368508e-302 | 0 |\n",
       "| 0 | 1.255178e+144 | 0 | 0 | 7.888714e-303 | 5.276246e-302 | 1.483093e-308 | 0 | 4.843548e-100 | 1.614362e-302 | 0 |\n",
       "| 0 | 1.056501e+144 | 0 | 0 | 1.463815e-303 | 3.801940e-302 | 5.487722e-308 | 0 |  1.355931e-99 | 2.639777e-302 | 0 |\n",
       "| 0 | 2.171380e+144 | 0 | 0 | 2.997213e-303 | 3.856706e-302 | 4.862166e-308 | 0 |  4.335400e-99 | 1.306470e-302 | 0 |\n",
       "| 0 | 3.444916e+144 | 0 | 0 | 3.222713e-303 | 2.173719e-302 | 5.441804e-308 | 0 |  4.385336e-99 | 1.093714e-302 | 0 |\n",
       "| 0 | 3.277033e+144 | 0 | 0 | 8.946544e-303 | 7.381324e-303 | 3.831026e-308 | 0 |  4.237025e-99 | 2.870760e-302 | 0 |\n",
       "| 0 | 2.375256e+144 | 0 | 0 | 1.140013e-302 | 7.352856e-303 | 3.616415e-308 | 0 |  4.599892e-99 | 1.012123e-302 | 0 |\n",
       "| 0 | 2.217816e+144 | 0 | 0 | 5.229429e-303 | 1.858480e-303 | 2.953806e-308 | 0 |  6.855735e-99 | 3.284830e-302 | 0 |\n",
       "| 0 | 7.813813e+143 | 0 | 0 | 2.479188e-302 | 2.253427e-303 | 2.908792e-308 | 0 |  4.284093e-98 | 7.682802e-302 | 0 |\n",
       "| 0 | 8.154483e+143 | 0 | 0 | 1.510394e-302 | 1.253921e-303 | 3.083610e-308 | 0 |  3.655518e-98 | 1.248841e-301 | 0 |\n",
       "| 0 | 6.155162e+143 | 0 | 0 | 9.708519e-303 | 1.256789e-303 | 2.998998e-308 | 0 |  8.630025e-99 | 5.137041e-302 | 0 |\n",
       "| 0 | 5.717270e+143 | 0 | 0 | 1.364811e-302 | 3.618841e-304 | 4.259334e-308 | 0 |  9.844238e-99 | 1.044006e-302 | 0 |\n",
       "| 0 | 1.325654e+144 | 0 | 0 | 1.159019e-302 | 3.555243e-304 | 2.116205e-308 | 0 |  8.980722e-99 | 5.469330e-303 | 0 |\n",
       "| 0 | 3.187082e+143 | 0 | 0 | 1.126228e-302 | 4.138938e-304 | 4.546589e-308 | 0 |  1.142863e-98 | 1.949048e-303 | 0 |\n",
       "| 0 | 1.382433e+143 | 0 | 0 | 5.523864e-304 | 1.976697e-304 | 3.266907e-307 | 0 |  3.173911e-99 | 6.340404e-304 | 0 |\n",
       "\n"
      ],
      "text/plain": [
       "    Y1  Y2            Y3  Y4  Y5            Y6            Y7            Y8 \n",
       "1   0   1.215130e+142 0   0   2.793068e-295 2.793068e-295 2.793068e-295 0  \n",
       "2   0   1.187310e+142 0   0   4.755710e-295 1.186560e-295 6.145261e-296 0  \n",
       "3   0   7.805206e+141 0   0   2.024504e-295 6.700094e-296 8.676613e-296 0  \n",
       "4   0   5.437949e+141 0   0   4.057125e-295 8.935600e-296 2.231381e-296 0  \n",
       "5   0   5.480044e+141 0   0   1.874846e-295 1.721622e-295 1.043514e-296 0  \n",
       "6   0   8.698185e+141 0   0   5.067863e-296 1.629863e-295 4.851646e-297 0  \n",
       "7   0   8.656196e+141 0   0   6.451520e-296 1.492961e-295 1.084705e-297 0  \n",
       "8   0   2.069204e+141 0   0   1.150784e-295 1.822083e-295 5.217276e-298 0  \n",
       "9   0   6.100067e+141 0   0   1.506189e-295 2.091316e-295 1.172255e-297 0  \n",
       "10  0   1.202963e+142 0   0   2.178270e-295 9.406781e-296 8.565935e-298 0  \n",
       "11  0   1.118249e+142 0   0   4.776749e-296 2.901347e-295 3.783882e-298 0  \n",
       "12  0   1.370099e+142 0   0   1.399276e-295 4.785086e-295 5.737635e-299 0  \n",
       "13  0   8.823773e+141 0   0   2.592464e-296 2.004593e-294 2.867393e-298 0  \n",
       "14  0   3.269629e+142 0   0   6.145476e-296 4.734512e-295 1.461304e-298 0  \n",
       "15  0   7.669523e+142 0   0   9.816670e-297 9.326966e-295 3.853594e-299 0  \n",
       "16  0   5.903525e+142 0   0   1.445472e-296 9.967019e-295 1.635429e-299 0  \n",
       "17  0   4.390418e+142 0   0   5.744933e-297 1.500674e-294 2.686873e-299 0  \n",
       "18  0   9.689287e+142 0   0   1.401983e-296 1.200373e-294 1.492392e-299 0  \n",
       "19  0   2.847748e+143 0   0   9.318911e-297 3.779565e-295 1.556275e-299 0  \n",
       "20  0   2.302062e+143 0   0   1.320577e-296 1.019241e-295 2.767060e-299 0  \n",
       "21  0   1.702895e+143 0   0   1.555386e-296 1.271064e-296 1.440509e-299 0  \n",
       "22  0   3.502352e+143 0   0   1.540693e-297 8.542344e-297 3.604824e-300 0  \n",
       "23  0   2.447367e+143 0   0   3.094581e-298 9.547908e-297 1.965181e-300 0  \n",
       "24  0   1.862198e+143 0   0   9.219635e-299 7.309931e-297 3.358475e-301 0  \n",
       "25  0   3.295069e+143 0   0   2.925627e-299 4.620692e-297 2.744398e-301 0  \n",
       "26  0   5.870060e+143 0   0   1.043499e-298 1.687481e-297 3.574772e-302 0  \n",
       "27  0   9.519732e+143 0   0   1.405758e-299 2.525146e-299 1.714314e-302 0  \n",
       "28  0   9.898609e+143 0   0   6.565097e-299 6.744314e-300 1.260531e-302 0  \n",
       "29  0   1.849046e+143 0   0   3.917448e-299 2.064595e-300 1.269698e-302 0  \n",
       "30  0   7.345389e+142 0   0   1.580585e-299 5.826607e-301 3.368440e-303 0  \n",
       "... ... ...           ... ... ...           ...           ...           ...\n",
       "35  0   1.925858e+143 0   0   1.407720e-299 5.116083e-300 8.077541e-304 0  \n",
       "36  0   8.668908e+143 0   0   8.275344e-299 5.416940e-301 2.202424e-304 0  \n",
       "37  0   3.405284e+143 0   0   4.056502e-299 5.915549e-301 3.673498e-304 0  \n",
       "38  0   5.145513e+143 0   0   3.850370e-299 4.049806e-301 3.138667e-304 0  \n",
       "39  0   3.276732e+143 0   0   2.217683e-299 1.791698e-301 4.675894e-304 0  \n",
       "40  0   6.336441e+143 0   0   9.508096e-300 4.299340e-301 7.366875e-304 0  \n",
       "41  0   4.293976e+143 0   0   1.821631e-299 5.272119e-301 2.396659e-304 0  \n",
       "42  0   3.346470e+143 0   0   7.487404e-300 2.001042e-301 3.839237e-305 0  \n",
       "43  0   3.191134e+143 0   0   4.382293e-300 3.895514e-301 2.610819e-305 0  \n",
       "44  0   3.967514e+143 0   0   1.486236e-300 1.016371e-301 2.855538e-305 0  \n",
       "45  0   2.166426e+143 0   0   9.035239e-301 9.469662e-302 3.108179e-306 0  \n",
       "46  0   4.519078e+143 0   0   1.522575e-301 1.779810e-301 1.813840e-306 0  \n",
       "47  0   1.225237e+144 0   0   3.026662e-302 5.410157e-302 6.112602e-307 0  \n",
       "48  0   6.749257e+143 0   0   9.810307e-302 5.765540e-302 2.669472e-307 0  \n",
       "49  0   5.266472e+143 0   0   6.595727e-302 4.540645e-302 7.239643e-308 0  \n",
       "50  0   7.866628e+143 0   0   1.906635e-302 5.368990e-302 5.597210e-308 0  \n",
       "51  0   1.255178e+144 0   0   7.888714e-303 5.276246e-302 1.483093e-308 0  \n",
       "52  0   1.056501e+144 0   0   1.463815e-303 3.801940e-302 5.487722e-308 0  \n",
       "53  0   2.171380e+144 0   0   2.997213e-303 3.856706e-302 4.862166e-308 0  \n",
       "54  0   3.444916e+144 0   0   3.222713e-303 2.173719e-302 5.441804e-308 0  \n",
       "55  0   3.277033e+144 0   0   8.946544e-303 7.381324e-303 3.831026e-308 0  \n",
       "56  0   2.375256e+144 0   0   1.140013e-302 7.352856e-303 3.616415e-308 0  \n",
       "57  0   2.217816e+144 0   0   5.229429e-303 1.858480e-303 2.953806e-308 0  \n",
       "58  0   7.813813e+143 0   0   2.479188e-302 2.253427e-303 2.908792e-308 0  \n",
       "59  0   8.154483e+143 0   0   1.510394e-302 1.253921e-303 3.083610e-308 0  \n",
       "60  0   6.155162e+143 0   0   9.708519e-303 1.256789e-303 2.998998e-308 0  \n",
       "61  0   5.717270e+143 0   0   1.364811e-302 3.618841e-304 4.259334e-308 0  \n",
       "62  0   1.325654e+144 0   0   1.159019e-302 3.555243e-304 2.116205e-308 0  \n",
       "63  0   3.187082e+143 0   0   1.126228e-302 4.138938e-304 4.546589e-308 0  \n",
       "64  0   1.382433e+143 0   0   5.523864e-304 1.976697e-304 3.266907e-307 0  \n",
       "    Y9            Y10           Y11\n",
       "1   6.836740e-96  2.793068e-295 0  \n",
       "2   2.376106e-96  1.454210e-295 0  \n",
       "3   3.951450e-96  1.237354e-295 0  \n",
       "4   1.357407e-96  1.501785e-295 0  \n",
       "5   1.063642e-96  4.692220e-295 0  \n",
       "6   3.825619e-96  7.687726e-295 0  \n",
       "7   1.309781e-96  5.052595e-295 0  \n",
       "8   2.468681e-96  7.219396e-295 0  \n",
       "9   2.745999e-96  1.632095e-294 0  \n",
       "10  5.446001e-96  3.224698e-295 0  \n",
       "11  6.361424e-96  2.045265e-295 0  \n",
       "12  1.202364e-95  2.189736e-295 0  \n",
       "13  3.032157e-95  8.122620e-296 0  \n",
       "14  1.983787e-95  1.257280e-295 0  \n",
       "15  4.231951e-95  4.323662e-296 0  \n",
       "16  5.156641e-95  2.499823e-296 0  \n",
       "17  1.178564e-95  8.681427e-296 0  \n",
       "18  3.688941e-96  7.445303e-296 0  \n",
       "19  1.562124e-96  6.970694e-296 0  \n",
       "20  4.551410e-96  4.869890e-296 0  \n",
       "21  3.139696e-96  5.573901e-296 0  \n",
       "22  1.856132e-96  2.811784e-296 0  \n",
       "23  7.184090e-97  6.390618e-297 0  \n",
       "24  9.796218e-97  1.218714e-296 0  \n",
       "25  9.533052e-97  2.002836e-297 0  \n",
       "26  1.543463e-96  3.264663e-297 0  \n",
       "27  2.059532e-96  2.991420e-297 0  \n",
       "28  1.055902e-96  2.237489e-297 0  \n",
       "29  1.060601e-96  5.874670e-298 0  \n",
       "30  3.578282e-96  3.834965e-298 0  \n",
       "... ...           ...           ...\n",
       "35   3.066516e-96 2.294926e-298 0  \n",
       "36   4.313420e-97 7.755364e-299 0  \n",
       "37   4.683306e-97 3.583295e-299 0  \n",
       "38   1.859243e-96 4.701828e-300 0  \n",
       "39   1.210274e-96 2.884976e-300 0  \n",
       "40   4.985211e-97 7.157361e-301 0  \n",
       "41   1.149113e-97 5.679973e-301 0  \n",
       "42   7.930051e-98 1.121549e-301 0  \n",
       "43   5.399128e-98 9.849269e-303 0  \n",
       "44   6.792340e-99 6.578958e-303 0  \n",
       "45   4.420937e-99 1.596134e-302 0  \n",
       "46   4.454108e-99 1.100628e-302 0  \n",
       "47  7.794922e-100 1.046148e-302 0  \n",
       "48   1.640562e-99 8.507125e-303 0  \n",
       "49  8.924392e-101 1.555921e-302 0  \n",
       "50  2.536269e-100 1.368508e-302 0  \n",
       "51  4.843548e-100 1.614362e-302 0  \n",
       "52   1.355931e-99 2.639777e-302 0  \n",
       "53   4.335400e-99 1.306470e-302 0  \n",
       "54   4.385336e-99 1.093714e-302 0  \n",
       "55   4.237025e-99 2.870760e-302 0  \n",
       "56   4.599892e-99 1.012123e-302 0  \n",
       "57   6.855735e-99 3.284830e-302 0  \n",
       "58   4.284093e-98 7.682802e-302 0  \n",
       "59   3.655518e-98 1.248841e-301 0  \n",
       "60   8.630025e-99 5.137041e-302 0  \n",
       "61   9.844238e-99 1.044006e-302 0  \n",
       "62   8.980722e-99 5.469330e-303 0  \n",
       "63   1.142863e-98 1.949048e-303 0  \n",
       "64   3.173911e-99 6.340404e-304 0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#横結合\n",
    "SPDE <- bind_cols(\n",
    "    SDE_Y1\n",
    "    , SDE_Y2\n",
    "    , SDE_Y3\n",
    "    , SDE_Y4\n",
    "    , SDE_Y5\n",
    "    , SDE_Y6\n",
    "    , SDE_Y7\n",
    "    , SDE_Y8\n",
    "    , SDE_Y9\n",
    "    , SDE_Y10\n",
    "    , SDE_Y11\n",
    ") %>%\n",
    "  apply(2, as.numeric) %>%\n",
    "  as.data.frame\n",
    "#列名変更\n",
    "colnames(SPDE) <- colnames(relation)\n",
    "#目視確認\n",
    "SPDE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "       Y1          Y2                   Y3          Y4          Y5            \n",
       " Min.   :0   Min.   :2.069e+141   Min.   :0   Min.   :0   Min.   : 0.000e+00  \n",
       " 1st Qu.:0   1st Qu.:5.491e+142   1st Qu.:0   1st Qu.:0   1st Qu.:1.000e-301  \n",
       " Median :0   Median :3.017e+143   Median :0   Median :0   Median :2.460e-299  \n",
       " Mean   :0   Mean   :5.289e+143   Mean   :0   Mean   :0   Mean   :3.920e-296  \n",
       " 3rd Qu.:0   3rd Qu.:6.440e+143   3rd Qu.:0   3rd Qu.:0   3rd Qu.:1.413e-296  \n",
       " Max.   :0   Max.   :3.445e+144   Max.   :0   Max.   :0   Max.   :4.756e-295  \n",
       "       Y6                   Y7                   Y8          Y9            \n",
       " Min.   : 0.000e+00   Min.   : 0.000e+00   Min.   :0   Min.   :9.000e-101  \n",
       " 1st Qu.:1.000e-301   1st Qu.: 0.000e+00   1st Qu.:0   1st Qu.: 8.890e-99  \n",
       " Median :1.600e-300   Median : 0.000e+00   Median :0   Median : 1.058e-96  \n",
       " Mean   :1.551e-295   Mean   :7.340e-297   Mean   :0   Mean   : 3.959e-96  \n",
       " 3rd Qu.:1.061e-295   3rd Qu.:2.707e-299   3rd Qu.:0   3rd Qu.: 3.085e-96  \n",
       " Max.   :2.005e-294   Max.   :2.793e-295   Max.   :0   Max.   : 5.157e-95  \n",
       "      Y10                  Y11   \n",
       " Min.   : 0.000e+00   Min.   :0  \n",
       " 1st Qu.: 0.000e+00   1st Qu.:0  \n",
       " Median :2.277e-298   Median :0  \n",
       " Mean   :9.706e-296   Mean   :0  \n",
       " 3rd Qu.:7.089e-296   3rd Qu.:0  \n",
       " Max.   :1.632e-294   Max.   :0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#要約統計量を求める。\n",
    "SPDE %>%\n",
    "  summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LSTM(Long Short Term Memory)\n",
    "- 時系列予測の精度評価を行う。\n",
    "- 時系列データ向けのDeepLearningである。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 出力保存先の相対パス指定"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "setwd(\"./1_output\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 関数定義の実行\n",
    "- 確率ベクトル毎に実行する。\n",
    "- LSTM(Long Short Term Memory)による幾何ブラウン運動の精度評価\n",
    "- 値が0以外の確率過程について精度評価を行う。\n",
    "- グラフ描画も関数定義に含む。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Saving 6.67 x 6.67 in image\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Saving 6.67 x 6.67 in image\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Saving 6.67 x 6.67 in image\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Saving 6.67 x 6.67 in image\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Saving 6.67 x 6.67 in image\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Saving 6.67 x 6.67 in image\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LSTM(SDE_Y1,  10)\n",
    "LSTM(SDE_Y2,  10)\n",
    "ggsave(\"LSTM_Y2.jpg\")\n",
    "# LSTM(SDE_Y3,  10)\n",
    "# LSTM(SDE_Y4,  10)\n",
    "LSTM(SDE_Y5,  10)\n",
    "ggsave(\"LSTM_Y5.jpg\")\n",
    "LSTM(SDE_Y6,  10)\n",
    "ggsave(\"LSTM_Y6.jpg\")\n",
    "LSTM(SDE_Y7,  10)\n",
    "ggsave(\"LSTM_Y7.jpg\")\n",
    "# LSTM(SDE_Y8,  10)\n",
    "LSTM(SDE_Y9,  10)\n",
    "ggsave(\"LSTM_Y9.jpg\")\n",
    "LSTM(SDE_Y10, 10)\n",
    "ggsave(\"LSTM_Y10.jpg\")\n",
    "# LSTM(SDE_Y11, 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 結果.csvの出力"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "fwrite(SPDE, \"panel_VAR_SPDE_LSTM_model.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# グラフ描画・出力\n",
    "1. ローデータの多変量時系列プロット\n",
    "1. 誤差項調整した多変量時系列プロット\n",
    "1. 相関行列の可視化による多重共線性の目視確認\n",
    "1. 幾何ブラウン運動の多変量時系列プロット"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ローデータの多変量時系列プロット"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Saving 6.67 x 6.67 in image\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#各グラフの作成\n",
    "fig_1  <- fig(raw_data, raw_data$Y1, \"最終エネルギー消費量(EJ)\", \"統計値\")\n",
    "fig_2  <- fig(raw_data, raw_data$Y2, \"温室効果ガス排出量(t-CO2換算)\", \"統計値\")\n",
    "fig_3  <- fig(raw_data, raw_data$Y3,  \"原発事故・異常事象(有無)\", \"統計値\")\n",
    "fig_4  <- fig(raw_data, raw_data$Y4, \"原油平均価格(実質US$/bbl)\", \"統計値\")\n",
    "fig_5  <- fig(raw_data, raw_data$Y5, \"実質GDP(US$/消費者物価指数: 2015年 = 1)\", \"統計値\")\n",
    "fig_6  <- fig(raw_data, raw_data$Y6, \"GDP比国債残高(%)\", \"統計値\")\n",
    "fig_7  <- fig(raw_data, raw_data$Y7, \"年間平均消費者物価指数(2015年 = 100)\", \"統計値\")\n",
    "fig_8  <- fig(raw_data, raw_data$Y8, \"年間平均完全失業率(%)\",\"統計値\")\n",
    "fig_9  <- fig(raw_data, raw_data$Y9, \"日経平均株価終値(実質円)\", \"統計値\")\n",
    "fig_10 <- fig(raw_data, raw_data$Y10, \"自民党の衆院議席獲得率(%)\", \"統計値\")\n",
    "fig_11 <- fig(raw_data, raw_data$Y11, \"気候変動対策に関する合意(有無)\", \"統計値\")\n",
    "fig_12 <- fig(raw_data, raw_data$id,  \"エネルギー基本計画の策定・改正(有無)\", \"統計値\")\n",
    "#一枚に集約して出力する。\n",
    "grid.arrange(fig_1, fig_2, fig_3, fig_4, fig_5, fig_6, fig_7, fig_8, fig_9, fig_10, fig_11, fig_12)\n",
    "ggsave(\"Multivariate_Time_Series_Plot_raw_data.jpg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 誤差項調整した多変量時系列プロット"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "geom_path: Each group consists of only one observation. Do you need to adjust\n",
      "the group aesthetic?\n",
      "\n",
      "geom_path: Each group consists of only one observation. Do you need to adjust\n",
      "the group aesthetic?\n",
      "\n",
      "geom_path: Each group consists of only one observation. Do you need to adjust\n",
      "the group aesthetic?\n",
      "\n",
      "geom_path: Each group consists of only one observation. Do you need to adjust\n",
      "the group aesthetic?\n",
      "\n",
      "geom_path: Each group consists of only one observation. Do you need to adjust\n",
      "the group aesthetic?\n",
      "\n",
      "geom_path: Each group consists of only one observation. Do you need to adjust\n",
      "the group aesthetic?\n",
      "\n",
      "geom_path: Each group consists of only one observation. Do you need to adjust\n",
      "the group aesthetic?\n",
      "\n",
      "geom_path: Each group consists of only one observation. Do you need to adjust\n",
      "the group aesthetic?\n",
      "\n",
      "geom_path: Each group consists of only one observation. Do you need to adjust\n",
      "the group aesthetic?\n",
      "\n",
      "geom_path: Each group consists of only one observation. Do you need to adjust\n",
      "the group aesthetic?\n",
      "\n",
      "geom_path: Each group consists of only one observation. Do you need to adjust\n",
      "the group aesthetic?\n",
      "\n",
      "geom_path: Each group consists of only one observation. Do you need to adjust\n",
      "the group aesthetic?\n",
      "\n",
      "Saving 6.67 x 6.67 in image\n",
      "\n",
      "geom_path: Each group consists of only one observation. Do you need to adjust\n",
      "the group aesthetic?\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#各グラフの作成\n",
    "fig_1  <- fig(panel, panel$Y1,  \"最終エネルギー消費量(EJ)\", \"対数差分系列\")\n",
    "fig_2  <- fig(panel, panel$Y2,  \"温室効果ガス排出量(t-CO2換算)\", \"対数差分系列\")\n",
    "fig_3  <- fig(panel, panel$Y3,  \"原発事故・異常事象(有無)\", \"対数差分プロビット写像\")\n",
    "fig_4  <- fig(panel, panel$Y4,  \"原油平均価格(実質US$/bbl)\", \"対数差分系列\")\n",
    "fig_5  <- fig(panel, panel$Y5,  \"実質GDP(US$/消費者物価指数: 2015年 = 1)\", \"対数差分系列\")\n",
    "fig_6  <- fig(panel, panel$Y6,  \"GDP比国債残高(%)\", \"対数差分系列\")\n",
    "fig_7  <- fig(panel, panel$Y7,  \"年間平均消費者物価指数(2015年 = 100)\",\"対数差分系列\")\n",
    "fig_8  <- fig(panel, panel$Y8,  \"年間平均完全失業率(%)\", \"対数差分系列\")\n",
    "fig_9  <- fig(panel, panel$Y9,  \"日経平均株価終値(実質円)\", \"対数差分系列\")\n",
    "fig_10 <- fig(panel, panel$Y10, \"自民党の衆院議席獲得率(%)\", \"対数差分系列\")\n",
    "fig_11 <- fig(panel, panel$Y11, \"気候変動対策に関する合意(有無)\", \"対数差分プロビット写像\")\n",
    "fig_12 <- fig(panel, panel$id,  \"エネルギー基本計画の策定・改正(有無)\", \"対数差分系列\")\n",
    "#一枚に集約して出力する。\n",
    "grid.arrange(fig_1, fig_2, fig_3, fig_4, fig_5, fig_6, fig_7, fig_8, fig_9, fig_10, fig_11, fig_12)\n",
    "ggsave(\"Multivariate_Time_Series_Plot_adjusted.jpg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 相関行列の可視化による多重共線性の目視確認"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Saving 6.67 x 6.67 in image\n",
      "\n",
      "geom_path: Each group consists of only one observation. Do you need to adjust\n",
      "the group aesthetic?\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "qgraph(cor(relation), edge.labels = TRUE)\n",
    "ggsave(\"relation_vector.jpg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 幾何ブラウン運動の多変量時系列プロット"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Saving 6.67 x 6.67 in image\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#classをtsからdata.frameに変換し、グラフの規格化を図る。\n",
    "plot_SDE_Y1  <- SDE_Y1  %>% ts_df\n",
    "plot_SDE_Y2  <- SDE_Y2  %>% ts_df\n",
    "plot_SDE_Y3  <- SDE_Y3  %>% ts_df\n",
    "plot_SDE_Y4  <- SDE_Y4  %>% ts_df\n",
    "plot_SDE_Y5  <- SDE_Y5  %>% ts_df\n",
    "plot_SDE_Y6  <- SDE_Y6  %>% ts_df\n",
    "plot_SDE_Y7  <- SDE_Y7  %>% ts_df\n",
    "plot_SDE_Y8  <- SDE_Y8  %>% ts_df\n",
    "plot_SDE_Y9  <- SDE_Y9  %>% ts_df\n",
    "plot_SDE_Y10 <- SDE_Y10 %>% ts_df\n",
    "plot_SDE_Y11 <- SDE_Y11 %>% ts_df\n",
    "#各グラフの作成\n",
    "fig_1  <- fig(plot_SDE_Y1,plot_SDE_Y1$value,   \"最終エネルギー消費量(EJ)\",\"時間微分係数の期待値\")\n",
    "fig_2  <- fig(plot_SDE_Y2,plot_SDE_Y2$value,   \"温室効果ガス排出量(t-CO2換算)\",\"時間微分係数の期待値\")\n",
    "fig_3  <- fig(plot_SDE_Y3,plot_SDE_Y3$value,   \"原発事故・異常事象(有無)\",\"時間微分係数の期待値\")\n",
    "fig_4  <- fig(plot_SDE_Y4,plot_SDE_Y4$value,   \"原油平均価格(実質US$/bbl)\",\"時間微分係数の期待値\")\n",
    "fig_5  <- fig(plot_SDE_Y5,plot_SDE_Y5$value,   \"実質GDP(US$/消費者物価指数: 2015年 = 1)\",\"時間微分係数の期待値\")\n",
    "fig_6  <- fig(plot_SDE_Y6,plot_SDE_Y6$value,   \"GDP比国債残高(%)\",\"時間微分係数の期待値\")\n",
    "fig_7  <- fig(plot_SDE_Y7,plot_SDE_Y7$value,   \"年間平均消費者物価指数(2015年 = 100)\",\"時間微分係数の期待値\")\n",
    "fig_8  <- fig(plot_SDE_Y8,plot_SDE_Y8$value,   \"年間平均完全失業率(%)\",\"時間微分係数の期待値\")\n",
    "fig_9  <- fig(plot_SDE_Y9,plot_SDE_Y9$value,   \"日経平均株価終値(実質円)\",\"時間微分係数の期待値\")\n",
    "fig_10 <- fig(plot_SDE_Y10,plot_SDE_Y10$value, \"自民党の衆院議席獲得率(%)\",\"時間微分係数の期待値\")\n",
    "fig_11 <- fig(plot_SDE_Y11,plot_SDE_Y11$value, \"気候変動対策に関する合意(有無)\",\"時間微分係数の期待値\")\n",
    "#一枚に集約して出力する。\n",
    "grid.arrange(fig_1, fig_2, fig_3, fig_4, fig_5, fig_6, fig_7, fig_8, fig_9, fig_10, fig_11)\n",
    "ggsave(\"Multivariate_Time_Series_Plot_GBM.jpg\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "4.1.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}