Quantitative-Suite

Welcome to the Data Analysis and Econometrics repository! This space is dedicated to providing examples and snippets for applying econometric methods using various tools such as Python, R, GRETL, Excel, and more.

Python & Econometrics

This repository contains Python code examples for applying econometric methods. Currently, it focuses on the Ordinary Least Squares (OLS) method for linear regression using the Statsmodels library.

Import Libraries:

import numpy as np
import statsmodels.api as sm

numpy is used for numerical operations. statsmodels is a library for estimating and testing statistical models.

Generate Sample Data:

np.random.seed(42)
x = np.random.rand(100, 1)  # Independent variable
y = 2 * x + 1 + 0.1 * np.random.randn(100, 1)  # Dependent variable with noise

numpy.random.seed(42) sets the random seed for reproducibility. x is a 1-dimensional array with 100 random values between 0 and 1. y is the dependent variable with a linear relationship to x and added random noise.

Add Constant Term:

Copy codeX = sm.add_constant(x)

sm.add_constant(x) adds a constant term (intercept) to the independent variable matrix. This is necessary for the OLS model to estimate the intercept.

Fit the OLS Model:

model = sm.OLS(y, X).fit()

sm.OLS(y, X).fit() fits the OLS model to the data.

Print Model Summary:

print(model.summary())

model.summary() prints a summary of the OLS regression results, including coefficients, standard errors, t-values, p-values, and R-squared.

Future Roadmap:

Implement other econometric methods (e.g., Time Series Analysis, Panel Data Analysis). Develop visualizations for better understanding and interpretation. Provide more detailed explanations and tutorials for each method. Incorporate real-world datasets for practical applications. Feel free to contribute, suggest improvements, or open issues for discussions!

Feel free to adjust the content based on your project's specific goals.

Econometrics & R

Generate Sample Data:

set.seed(42): Sets the random seed for reproducibility.
x <- runif(100): Generates a vector with 100 random values between 0 and 1.
y <- 2 * x + 1 + 0.1 * rnorm(100): Creates the dependent variable with a linear relationship to x and adds some random noise.

Add Constant Term:

X <- cbind(1, x): Adds a constant term (intercept) to the independent variable matrix. This is necessary for the OLS model to estimate the intercept.

Fit the OLS Model:

model <- lm(y ~ X): Fits the OLS model to the data.

Print Model Summary:

summary(model): Prints a summary of the OLS regression results, including coefficients, standard errors, t-values, p-values, and R-squared.