In case links may die in the future, download all available pdf files as soon as possible.

Template for thesis

Download the the .zip file from the release page.

or from Overleaf-gallery

Ruby script to convert wordpress xml to markdown.

https://gist.github.com/thanhqtran/6529293936987c97441d19e7b2c4d588

General tips

EconPhD Lecture notes:

http://econphd.econwiki.com/notes.htm

EconPhD Textbooks:

http://econphd.econwiki.com/books.htm

How to build an economic model in your spare time (Hal Varian):

https://people.ischool.berkeley.edu/~hal/Papers/how.pdf

Lecture Notes

Macroeconomics

General:

1. Groth (Copenhagen):

https://web.econ.ku.dk/okocg/VM/VM-general/Material/Chapters-VM.htm

• One file (from Ch6 onwards):

https://web2.econ.ku.dk/okocg/VM/VM16/Lectures%20and%20lecture%20notes/Lecture%20Notes%20in%20one%20file%20VM%202016.pdf

• Errata:

https://web2.econ.ku.dk/okocg/VM/VM16/Material/Errata%20to%20lecture%20notes%20VM%202016.htm

• His course:

https://web2.econ.ku.dk/okocg/VM/VM16/Advanced%20Macro-2016.htm

• His exercises:

https://www.economics.ku.dk/staff/vip/?pure=en%2Fpersons%2Fchristian-groth(bc3cc402-5f91-49e7-a653-860ed5ca1848)%2Fpublications.html&filter=education

• Bonus (his links, debates etc.):

https://web2.econ.ku.dk/okocg/VM/VM-general/diverse_supplerende_materiale_og_links.htm

2. Acemoglu (MIT) growth:

https://www.theigc.org/wp-content/uploads/2016/06/acemoglu-2007.pdf

3. William (WashU Louis):

https://www.albany.edu/~am755146/teaching/aeco601/Williamson%20notes.pdf

4. UC Irvine (concise & clear):

http://keeganskeate.com/pdfs/macroeconomics.pdf

5. Krueger (Penn):

https://perhuaman.files.wordpress.com/2014/06/macrotheory-dirk-krueger.pdf

6. Le-Van Cuong (Paris):

https://sites.google.com/site/cafeseminaire/courses/4-some-macroeconomic-growth-models

7. Krusell (Yale):

http://www.econ.yale.edu/smith/econ510a/book.pdf

8. Whelan (Dublin) (undergrad):

https://www.karlwhelan.com/Macro2/Whelan-Lecture-Notes.pdf

9. Sims (Notre Dame) (intermediate):

https://www3.nd.edu/~esims1/gls_int_macro.pdf

10. Nymoen (Oslo) : (intro to dynamics)

https://www.uio.no/studier/emner/sv/oekonomi/ECON4410/h08/undervisningsmateriale/h08_3410_120808.pdf

DSGE:

1. Hnatkovska (UBC):

   https://www.theigc.org/wp-content/uploads/2015/09/ISI2_2015.pdf

2. Bilbiie (Oxford):

   https://www.nuffield.ox.ac.uk/Users/Bilbiie/teaching_files/notes_oxford_final.pdf

3. Stata:

   https://blog.stata.com/2018/04/23/dynamic-stochastic-general-equilibrium-models-for-policy-analysis/

4. den Haan (lots of numerical treatment):

   http://econ.lse.ac.uk/staff/wdenhaan/notes.htm

RBC:

1. Wieland (UC San Diego):

   https://econweb.ucsd.edu/~gramey/210C/Wieland_210C_S18_Lecture_Notes.pdf

2. Carroll (JHU):

   https://www.econ2.jhu.edu/people/ccarroll/public/LectureNotes/DSGEModels/RBC-Prescott.pdf

3. another by his:

   https://www.econ2.jhu.edu/people/ccarroll/public/LectureNotes/

4. Solomon (Montreal):

   https://mpra.ub.uni-muenchen.de/70321/8/MPRA_paper_70321.pdf

New Keynesian:

1. Blanchard:

   https://ocw.mit.edu/courses/economics/14-452-macroeconomic-theory-ii-spring-2007/lecture-notes/slides08.pdf

2. Brugnolini:

   https://lucabrugnolini.github.io/teaching/LB_notes.pdf

3. Collection:

   https://sites.google.com/site/buchananhodgman/lecture-notes

Production network propagation shock:

1. Leontief:

   https://www.math.umd.edu/~immortal/MATH401/ch_leontief.pdf

2. Empirical:

   https://econweb.ucsd.edu/~vramey/research/Shocks_HOM_Ramey_published_corrected.pdf?mod=article_inline

3. Primer:

   https://vasco-m-carvalho.github.io/pdfs/ProductionNetworks.pdf

4. Acemoglu:

   https://economics.mit.edu/files/13466

(his slide):

https://economics.mit.edu/files/9790

5. Altinoglu:

   https://www.sciencedirect.com/science/article/pii/S0304393218305610?casa_token=Q6sqxbvRCT4AAAAA:TPV29GjPUL1wOywlxzIQCc8OEH2pNTb-D8QmY49IXCqe-KASR5TMSpYgBC7ebLEgpTBRlYbRIQ

Math for Econs

1. Dawnkins (Lamar) Calculus + Optimization:

https://tutorial.math.lamar.edu/

2. Groth's exercises (Copenhagen)

https://www.economics.ku.dk/staff/vip/?pure=en%2Fpersons%2Fchristian-groth(bc3cc402-5f91-49e7-a653-860ed5ca1848)%2Fpublications.html&page=6

3. Le-Van Cuong (Paris):

https://sites.google.com/site/cafeseminaire/courses/maths-for-economists](https://sites.google.com/site/cafeseminaire/courses/maths-for-economists)

4. Reshef (Virginia) / based on Alpha Chiang (1967)'s "Fundamental Methods of Mathematical Economics"

http://www.parisschoolofeconomics.com/reshef-ariell/Econ509/Lectures509.pdf

5. Mitra (Cornell) / have problem sets + solutions

https://pages.nyu.edu/debraj/TapanMitra/Lectures/LectureNotes2011.pdf

6. Quick tips/ summaries:

http://faculty.bcitbusiness.ca/kevinw/5701/5701_Lecture_Notes-14-3.htm

7. List of well-known closed-form results in Economics (Zevelev, 2014):

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2354226

Youtube

Opencoursewave courses

1. Principle of Macroeconomics (MSU):

https://youtube.com/playlist?list=PLdLiRaajwSXRcJxAeIHjVGukaJZoJtkXz

2. Principle of Microeconomics (MIT):

https://youtube.com/playlist?list=PL61533C166E8B0028

3. Econometrics (UO):

https://youtube.com/playlist?list=PLD15D38DC7AA3B737

4. Development Econs (MIT):

https://youtube.com/playlist?list=PLUl4u3cNGP63-t0r0aC3noJiIOmj33S_Q

5. Math for Econs (UCI):

https://youtube.com/playlist?list=PLqOZ6FD_RQ7n8yvjW0DAxRAmou8EOzbpD

6. Single Variable Calculus (MIT):

https://youtube.com/playlist?list=PL590CCC2BC5AF3BC1

7. Multivariable Calculus (MIT):

https://youtube.com/playlist?list=PL4C4C8A7D06566F38

8. Highlights of Calculus (MIT):

https://youtube.com/playlist?list=PLBE9407EA64E2C318

9. Differential Equations (MIT):

https://youtube.com/playlist?list=PLUl4u3cNGP63oTpyxCMLKt_JmB0WtSZfG

10. Linear Algebra (MIT):

https://youtube.com/playlist?list=PL49CF3715CB9EF31D

11. Convex Optimization (Stanford):

https://youtube.com/playlist?list=PL3940DD956CDF0622

12. Probability (MIT):

https://youtube.com/playlist?list=PLUl4u3cNGP60hI9ATjSFgLZpbNJ7myAg6

13. Optimization (CMU ML):

https://www.youtube.com/playlist?list=PL7y-1rk2cCsDOv91McLOnV4kExFfTB7dU

14. Applied Probability (MIT):

https://youtube.com/playlist?list=PLUl4u3cNGP61MdtwGTqZA0MreSaDybji8

15. Advanced Macro (Berlin):

https://youtube.com/playlist?list=PLJZlW3ik4xixAhVnY0aaTrz72XCZsygEA

Teach you how to solve/understand things:

1. Constantin Burgi (GWU):

   https://youtube.com/playlist?list=PLXbXFxCCVuUWgdvabVrFMjgXFaArAdDrF

2. David Jinkins (Copenhagen):

   https://www.youtube.com/channel/UCxWh3hcY6Jbnoza3WLCjMXA/videos

3. Jeremy Choquette (Clemson):

   https://www.youtube.com/channel/UCm0Kudqbls9N08Cyuag2rPg/playlists

4. Thomas J Sargent:

   https://www.youtube.com/channel/UCA8WXlZURaLVgp_Foo3NUlg/featured

5. 3B1B:

   https://www.youtube.com/channel/UCYO_jab_esuFRV4b17AJtAw

Technical stuff

LaTeX

• Beamer:

  https://www.overleaf.com/learn/latex/Beamer_Presentations:A_Tutorial_for_Beginners(Part_2)%E2%80%94Lists,_Columns,_Pictures,_Descriptions_and_Tables

• Tikz:

  https://sites.google.com/site/kochiuyu/Tikz

• Cookbook:

  https://latex-cookbook.net/

• Templates:

  https://www.latextemplates.com/

• Elsevier template:

  http://mirrors.ctan.org/macros/latex/contrib/els-cas-templates.zip

R/Python/Julia

• Teach yourself R:

https://teacher.arawles.co.uk/

• Cookbook R:

https://rc2e.com/

• R For DS:

https://r4ds.had.co.nz/

• DS at the command line:

https://www.datascienceatthecommandline.com/1e/

• Bayesian:

http://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/

• Think Python:

https://greenteapress.com/thinkpython2/thinkpython2.pdf

• Web scraping:

https://yanfei.site/docs/dpsa/references/PyWebScrapingBook.pdf

companion code:

https://github.com/PacktPublishing/Python-Web-Scraping-Cookbook

• Data wrangling:

https://github.com/Jffrank/Books/blob/master/Python%20for%20Data%20Analysis.%20Data%20Wrangling%20with%20Pandas%2C%20NumPy%2C%20and%20IPython%20(2017%2C%20O%E2%80%99Reilly).pdf

• Think Julia:

https://benlauwens.github.io/ThinkJulia.jl/latest/book.html

• Matlab mega course:

https://github.com/jousefm/Mega-Course-MATLAB

• Physical Modeling with Matlab:

https://greenteapress.com/wp/physical-modeling-in-matlab/

Cheatsheets

• (R)Markdown: https://rmarkdown.rstudio.com/lesson-15.HTML
• LaTeX: https://wch.github.io/latexsheet/
• Python: https://hakin9.org/python-cheat-sheet-for-hackers-and-developers/
• Pandas: https://datacamp-community-prod.s3.amazonaws.com/dbed353d-2757-4617-8206-8767ab379ab3
• Pandas2: https://www.slideshare.net/ACASH1011/pandas-cheat-sheet
• Matplotlib: https://github.com/matplotlib/cheatsheets or https://storage.googleapis.com/kaggle-forum-message-attachments/994125/16813/Python_Matplotlib_Cheat_Sheet.pdf
• (R) Wrangling: https://www.rstudio.com/wp-content/uploads/2015/02/data-wrangling-cheatsheet.pdf

Computational Codes and Models

• Numedig (Copenhagen): http://www.tjeconomics.com/numedig/
• Dynare by Pfeifer: https://github.com/JohannesPfeifer/DSGE_mod
• Quantecon: https://quantecon.org/
• Stata Parameters Estimation: https://blog.stata.com/2017/07/11/estimating-the-parameters-of-dsge-models/#ref
• Computational Econs: https://juejung.github.io/jdocs/Comp/html/index.html
• PyEcons: https://github.com/davidrpugh/pyeconomics/wiki/OLG-Models
• CompEcon: https://github.com/PaulFackler/CompEcon (Python: https://github.com/randall-romero/CompEcon Book: [http://www.untag-smd.ac.id/files/Perpustakaan_Digital_1/FINANCE%20Applied%20Computational%20Economics%20And%20Finance.%5B2004.ISBN0262633094%5D.pdf](http://www.untag-smd.ac.id/files/Perpustakaan_Digital_1/FINANCE Applied Computational Economics And Finance.[2004.ISBN0262633094].pdf))
• Torres (Malaga) DSGE: https://vernonpress.com/file/740/d1714638ebf2bb09a94aa3d85778e822/1440333327.zip
• IRIS Macro Toolbox: https://iris.igpmn.org/install/matlab/
• De La Croix HP models: https://perso.uclouvain.be/david.delacroix/articles.html
• R.Econ.Dyn. papers' code: https://ideas.repec.org/s/red/ccodes.html

Economics Writing:

• Nikolov (Harvard): https://www.people.fas.harvard.edu/~pnikolov/resources/writingtips.pdf
• Todo (Waseda): http://www.f.waseda.jp/yastodo/laboratory/TipstoWriteAThesis20141119.pdf
• GPEM (TeX tutorial): https://www.econ.tohoku.ac.jp/english/files2020/GPEM_Latex.pdf
• Impact factor: https://ideas.repec.org/top/top.journals.simple.html
• Paper evaluation through the eyes of a referee: [https://web2.econ.ku.dk/okocg/Forside/Evaluating%20a%20research%20article.pdf](https://web2.econ.ku.dk/okocg/Forside/Evaluating a research article.pdf)

Selected online courses

MIT

• 1. Intro to coding https://www.edx.org/course/introduction-to-computer-science-and-programming-7
• 2. Python and data https://www.edx.org/course/introduction-to-computational-thinking-and-data-4
• 3. Micro (Advanced Placement) https://www.edx.org/course/ap-microeconomics
• 4. Macro (Advanced Placement) https://www.edx.org/course/ap-macroeconomics
• 5. Prob. and Stats. https://ocw.mit.edu/courses/mathematics/18-05-introduction-to-probability-and-statistics-spring-2014/calendar/ or: https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-041-probabilistic-systems-analysis-and-applied-probability-fall-2010/ (Video)
• 6. Comp & DS https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-0002-introduction-to-computational-thinking-and-data-science-fall-2016/
• 7. Intermediate Macro: https://ocw.mit.edu/courses/economics/14-05-intermediate-macroeconomics-spring-2013/lecture-notes/
• 8. Advanced Macro 1: https://ocw.mit.edu/courses/economics/14-461-advanced-macroeconomics-i-fall-2012/
• 9. Advanced Macro 2: https://ocw.mit.edu/courses/economics/14-462-advanced-macroeconomics-ii-spring-2004/[https://ocw.mit.edu/courses/economics/14-462-advanced-macroeconomics-ii-spring-2007/index.htm](https://ocw.mit.edu/courses/economics/14-462-advanced-macroeconomics-ii-spring-2007/index.htm)

IMF Courses:

• 1. https://www.edx.org/course/financial-programming-and-policies-part-1-macroeco
• 2. https://www.edx.org/course/financial-programming-and-policies-part-2-progra-2
• 3. https://www.edx.org/course/compilation-basics-for-macroeconomic-statistics
• 4. https://www.edx.org/course/public-sector-debt-statistics
• 5. https://www.edx.org/course/financial-market-analysis
• 6. https://www.edx.org/course/macroeconometric-forecasting-2
• 7. https://www.edx.org/course/monetary-policy-analysis-and-forecasting

Essential for Econs:

• 1. Optimization: https://agecon2.tamu.edu/people/faculty/woodward-richard/642/notes/default.htm
• 2. Dynamic Programming: https://absalon.ku.dk/courses/25988/wiki
• 3. Linear Algebra: https://ocw.mit.edu/courses/mathematics/18-06sc-linear-algebra-fall-2011/ax-b-and-the-four-subspaces/the-geometry-of-linear-equations/
• 4. SIngle Variable Calculus: https://ocw.mit.edu/courses/mathematics/18-01sc-single-variable-calculus-fall-2010/
• 5. Multi-variable Calculus: https://ocw.mit.edu/courses/mathematics/18-02sc-multivariable-calculus-fall-2010/
• 6. Differential Equations: https://ocw.mit.edu/courses/mathematics/18-03sc-differential-equations-fall-2011/
• 7. Development Economics (Macroeconomics): https://ocw.mit.edu/courses/economics/14-772-development-economics-macroeconomics-spring-2013/
• 8. Econometrics with R-code: https://ocw.mit.edu/courses/economics/14-382-econometrics-spring-2017/
• 9. Economic Growth: https://ocw.mit.edu/courses/economics/14-452-economic-growth-fall-2016/
• 10. Time-series analysis with Matlab: https://ocw.mit.edu/courses/economics/14-384-time-series-analysis-fall-2013/index.htm
• 11. Maxtrix method in Data Analysis: https://ocw.mit.edu/courses/mathematics/18-065-matrix-methods-in-data-analysis-signal-processing-and-machine-learning-spring-2018/
• 12. Principles of Econs: https://online.stanford.edu/courses/sohs-yeconschool-principles-economics
• 13. Convex Optimization: https://online.stanford.edu/courses/soe-yeecvx101-convex-optimization

Data Science

• 1. Harvard (R): https://www.edx.org/professional-certificate/harvardx-data-science
• 2. IBM (SQL+Python): https://www.edx.org/professional-certificate/ibm-data-science

Tohoku University DS training (Intro to Python):**

• Intro to Python: https://sites.google.com/view/gsis-ilo-skill-up-training/home
• Training Camp 1: https://sites.google.com/view/gsis-ilo-trainingcampi/home
• Linear Regression: https://sites.google.com/view/gsis-ilo-trainingcampi/lessons/lesson-v-supervised-learning/linear-regression?authuser=0
• Intro to Data Science: https://sites.google.com/view/gsis-ilo-datasciencebasic/lessons?authuser=0