Guide to Efficient RA Computational Work in Economics

Comp Econ Lectures

(programming-language-neutral)

• Hans-Martin von Gaudecker - Effective programming practices for economists
• Jesús Fernández-Villaverde - Computational Methods for Economists
• Richard W. Evans, et al. - Open Source Economics Laboratory (OSE Lab) Boot Camp 2019, 2018, 2017
• Kenneth Judd - Computational Economics 2020

Data & Code & Reproducibility

• Gentzkow, M., & Shapiro, J. M. (2014). Code and data for the social sciences: A practitioner’s guide. Chicago, IL: University of Chicago. (Github manual)
• Knittel, C. R., & Metaxoglou, K. (2016). Working with Data: Two Empiricists’ Experience. Journal of Econometric Methods, 7(1).
• Christensen, Garret S. and Edward Miguel, "Transparency, Reproducibility, and the Credibility of Economics Research," Journal of Economic Literature, 56:3 pp. 920-980 (Sep. 2018).
• Ljubica Ristovska - Coding for Economists: A Language-Agnostic Guide to Programming for Economists
• Templates for Reproducible Research Projects in Economics
• Wilson, G., Bryan, J., Cranston, K., Kitzes, J., Nederbragt, L., & Teal, T. K. (2017). Good enough practices in scientific computing. PLoS computational biology, 13(6), e1005510.
• The Plain Person’s Guide to Plain Text Social Science - Kieran Healy
• Data and Code Guidance by Data Editors / A template README for social science replication packages (see also the AEA guidance on Data and Code) (github)
• Best Practices when Writing Code
• Patrick Ball - The Task Is A Quantum Of Workflow and Principled Data Processing
• The Turing Way (a lightly opinionated guide to reproducible data science)
• Fernando Hoces - Accelerating Computational Reproducibility in Economics
• A template README for social science replication packages

Tips for Comp Econ Work

• Michael Keane - Practical Issues in Structural Estimation
• Chris Taber - Estimation of Policy Counterfactuals / Structural Estimation
• Tony Smith - Tips for Doing Computational Work in Economics

Python - Econ Lectures

• QuantEcon - Python (also here)
• QuantEcon DataScience
• Richard W. Evans -
  • Perspectives on Computational Modeling for Economics 2020
  • Perspectives on Computational Research in Economics 2020
  • Structural Estimation 2020
  • Git and GitHub tutorial
• Jason DeBacker - Computational Methods for Economists 2017, 2019
• Fedor Iskhakov - Dynamic programming and structural estimation
• Fedor Iskhakov - Foundations of Computational Economics
• Jeppe Druedahl - Introduction to Programming and Numerical Analysis
• Kevin Sheppard - Introduction to Python for Econometrics, Statistics and Numerical Analysis
• NYU-Data-Bootcamp
• Matheus Facure - Python Causality Handbook
• OpenSourceEconomics - Scientific Computing
• OpenSourceEconomics - Microeconometrics

Python - Other Tutorials

• Research Software Engineering with Python
• Reproducible and Collaborative Data Science
• Jake VanderPlas - Python Data Science Handbook / A Whirlwind Tour of Python
• Nick Eubank - Data Analysis in Python
• Tom Augspurger - Modern Pandas
• Introduction to Python for Science
• Computational Statistics in Python
• Python computational labs
• Real Python Tutorials
• Scipy Lecture Notes
• Econometrics in Python
• Claudio Jolowicz - Hypermodern Python
• Nicolas P. Rougier - From Python to Numpy
• Nicolas P. Rougier - Scientific Visualization – Python & Matplotlib / Matplotlib cheat sheet
• Introduction to Mathematical Computing with Python and Jupyter
• Introduction to Data Science and Programming
• Bayesian Computing Course
• Arthur Turrell - Coding for Economists
• Cliburn - Computational Statistics and Statistical Computing
• A Python version of Miranda and Fackler's CompEcon toolbox

Julia - Econ Lectures

• QuantEcon - Julia
• Paul Schrimpf - Computational Economics with Data Science Applications 2019, 2020
• Paul Schrimpf - UBC ECON567 IO
• Ivan Rudik - Environmental and Resource Economics (computational methods for economics)
• Tyler Ransom - Econometrics III
• Michael Creel - Econometrics lecture notes with examples using the Julia language
• Bradley J. Setzler - An Introduction to Structural Econometrics in Julia
• Florian Oswald - Computational Economics for PhDs (2021)
• Tutorials on Topics in Julia Programming

Julia - Other Tutorials

• Statistics with Julia: Fundamentals for Data Science, Machine Learning and Artificial Intelligence - H.Klok, Y.Nazarathy
• Think Julia: How to Think Like a Computer Scientist - Ben Lauwens
• Introduction to Computational Thinking - MIT
• Tutorials for Scientific Machine Learning and Differential Equations
• Algorithms for Decision Making

R - Econ Lectures

• Maximilian Kasy - Advanced Econometrics and Machine Learning: 2019, 2000 / collection of useful computation links on R and ML
• Nick Huntington-Klein - Economics, Causality, and Analytics / R resources / Causal Inference
• Nick Huntington-Klein et al. - Library of Statistical Techniques
• Francis J. DiTraglia - Statistical Learning and Causal Inference for Economics
• Tom O'Grady - Causal-Inference-for-Beginning-Undergraduates
• Grant McDermott - Data science for economists / Big Data in Economics
• Christoph Hanck, et al. - Introduction to Econometrics with R
• Ed Rubin - PhD Econometrics (III) with R / Masters-level applied econometrics with R / Introduction to Econometrics with R
• Tyler Ransom - Econometric Analysis (U)
• Hans H. Sievertsen - Applied Economics with R
• Matt Woerman - Topics in Advanced Econometrics
• Sebastian Kranz - Empirical Economics with R / Market Analysis with Econometrics and Machine Learning

R - Other Tutorials

• Garrett Grolemund & Hadley Wickham - R for Data Science
• Hadley Wickham - Advanced R
• The tidyverse style guide
• dlab-berkeley - R-Fundamentals / ...
• Jeffrey Arnold - R Code for Mastering ’Metrics
• Gina Reynolds - R Flipbook Textbook
• R for Health Data Science
• A ModernDive into R and the Tidyverse
• Andrew Heiss - Program Evaluation for Public Service / Data Visualization
• R Data Science Tutorials (resources)
• Telling Stories With Data

Machine Learning For Economists

• Dario Sansone - Machine Learning for Economists (Resources)
• Tyler Ransom - Data Science for Economists 2020, 2021 (R) / Introduction to machine learning for social scientists
• Ed Rubin and Connor Lennon - Prediction and machine-learning in econometrics (R)
• Itamar Caspi - A course in machine learning for economists (R)
• Stephen Hansen - machine_learning_economics / text-mining-tutorial (Python)
• Jérémy L'Hour - Machine Learning for Econometrics (R)
• Le Wang - Machine Learning and Causal Inference (R)
• Kathy Baylis et al. - Machine learning in applied economics (Python)
• Dawie van Lill - Data Science for Economics (Python)
• Economics and Data Science (Resources)
• Jonathan Hersh - Introduction to Machine Learning (R)

Machine Learning Lectures

• Andreas C. Müller - Applied Machine Learning Spring 2020 (python)
• Tyler Folkman - Applied Machine Learning (python)

Machine Learning - Other Resources

• Chris Albon - Data Science & Artificial Intelligence (Resources)
• Practical tips for machine learning practitioners
• Hands-On Machine Learning with R
• Deep Learning Drizzle (Links)

NLP

• Oxford Deep NLP 2017 course
• NYU Text as Data Lab (R)

Stata

• Fundamentals of data analysis and visualization
• Economics Lesson with Stata
• Statistical Analysis - UCLA
• Statistics cheatsheet (R, Python, Stata) - QuantEcon
• Stata to Python Equivalents - Daniel M. Sullivan
• Pandas comparison with Stata
• Stata Coding Guide - Julian Reif

Basic CS Lectures

• The Missing Semester of Your CS Education
• Software Carpentry
• Harvard CS50

Bash

• pure bash bible
• useful bash scripts shared by economists: Ed Rubin, John Horton

OS

• macOS Setup Guide
• Mac Setup
• The Unix Shell - Software Carpentry
• Corey Schafer's youtube channel (very beginner-friendly videos for almost all basic things about mac, python, git, etc.)
• Terminal: here, here, here
• The Art of Command Line

Git

• git for social science students (not software developers) - Shiro Kuriwaki
• Version Control for Economists - Wei Yang Tham
• Git for Economists - Frank Pinter
• Git, GitHub, and Version Control - QuantEcon
• Git and GitHub tutorial chapter - Richard Evans
• Bryan, J. (2018). Excuse me, do you have a moment to talk about version control?. The American Statistician, 72(1), 20-27.
• Bruno, R. (2016). Version control systems to facilitate research collaboration in economics. Computational Economics, 48(3), 547-553.
• git + LaTeX workflow - stackoverflow
• Pro Git book - Scott Chacon & Ben Straub
• Git Tutorials and Training - Atlassian
• Using Git & GitHub Guides - github
• Git & Version Control FAQ - git-tower
• Happy Git and GitHub for the useR - Jenny Bryan
• Git for Scientists - Miles McBain
• Flight rules for git

Latex

• The Not So Short Introduction to LATEX - Tobias Oetiker
• A simple guide to LaTeX - Step by Step
• Overleaf guides to LaTeX
• Tips + Tricks with Beamer for Economists - Paul Goldsmith-Pinkham
• Template-based introductory guide to LaTeX for Economics
• A LaTeX Template for Economics Papers

Markdown

• The Markdown Guide
• Markdown Reference - typora (some Japanese introduction on typora)

Regex

• Learn regex the easy way
• Online regex tester and debugger (also this)

Visualization

• Schwabish, J. A. (2014). An economist's guide to visualizing data. Journal of Economic Perspectives, 28(1), 209-34.
• Some Data visualizations in Python
• Python Plotting for Exploratory Data Analysis
• from Data to Viz - The Python Graph Gallery / The R Graph Gallery
• Kieran Healy - Data Visualization - A practical introduction
• ggplot2: Elegant Graphics for Data Analysis

Unsorted

• (a real) Econ RA Guide
• Aruoba, S. B., & Fernández-Villaverde, J. (2018). A comparison of programming languages in economics.
• Awesome Scientific Writing
• awesome-causality-algorithms
• MATLAB–Python–Julia cheatsheet
• Merely Useful (python and r lecture)
• Computing in Optimization and Statistics 2017
• Hernán MA, Robins JM (2020). Causal Inference: What If. (with computer code of stat, R, python)
• The Programming Historian (lessons on digital tools, methods, and research processes)

Other Econ Lectures on Github

• Analysis of Human Capital - Labor Economics / Econometrics of Human Capital
• Joao B. Duarte - Advanced Macro
• Bryan S. Graham - Econometrics
• Alfred Galichon - Advanced Topics in Microeconometrics
• Alfred Galichon - 'math+econ+code' series
• SciencePo - (UG) Econometrics
• Dietz Vollrath - (UG) Growth
• Ömer Özak - Economic Growth and Comparative Development / Macroeconomics II
• Jonathan Dingel - International Macroeconomics and Trade
• David Ubilava - (UG) Agricultural Markets
• mostly-harmless-replication in Stata, R, Python and Julia
• Ivan Rudik - Environmental and Resource Economics
• Scott Cunningham - causal-inference-class
• Paul Goldsmith-Pinkham - Applied Empirical Methods
• José Luis Montiel Olea - IntroEconometrics-Ph.D / TimeSeries-UG

Links to Other Econ Resources

• Jennifer Doleac - Advice for current and aspiring academic economists
• Amanda Y. Agan - Writing, Presentation, and Refereeing Advice
• Ryan B Edwards - Resource
• Masayuki Kudamatsu - Tips 4 Economists
• Jonathan Dingel - Research resources that I recommend / Advice resource
• AEA-CSWEP - Mentoring Reading Materials
• Mizuhito Suzuki - Resources
• Christoph Kronenberg - Resources