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
• Benjamin Vatter & Gaston Illanes - Coding Primer: A Reference Guide
• 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.
• Kieran Healy - The Plain Person’s Guide to Plain Text Social Science
• 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
• The Good Research Code Handbook

(see replication for more)

Tips or Lectures for Structural Econ

• Michael Keane - Practical Issues in Structural Estimation
• Chris Taber - Estimation of Policy Counterfactuals / Structural Estimation
• Tony Smith - Tips for Doing Computational Work in Economics
• Australian Summer School in Dynamic Structural Econometrics
• Robert A. Miller - STRUCTURAL ECONOMETRICS

Applied Microeconometrics & Casual Inference

• Paul Goldsmith-Pinkham - Applied Empirical Methods
• Scott Cunningham - Causal Inference The Mixtape / Mixtape-Sessions
• Nick Huntington-Klein - The Effect: An Introduction to Research Design and Causality / Library of Statistical Techniques
• Christine Cai - Literature on Recent Advances in Applied Micro Methods
• Asjad Naqvi - Diff-in-Diff-Literature
• mostly-harmless-replication in Stata, R, Python and Julia
• Hernán MA, Robins JM (2020). Causal Inference: What If.
• Brady Neal - Introduction to Causal Inference from a Machine Learning Perspective
• David Childers - Causal Econometrics
• Kosuke Imai - Causal Inference with Applications
• Matheus Facure Alves - Causal Inference for The Brave and True

Python - Econ Lectures

• QuantEcon - Python (also here)
• QuantEcon DataScience
• Richard W. Evans - Perspectives on Computational Modeling for Economics 2020
• Richard W. Evans - Perspectives on Computational Research in Economics 2020
• Richard W. Evans - Structural Estimation 2020
• Jason DeBacker - Computational Methods for Economists 2017, 2019, 2021
• 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 - Data Science (Microeconometrics)
• CompEcon: A Python version of Miranda and Fackler's CompEcon toolbox
• Jan Boone - Doing economics with python
• Arthur Turrell - Coding for Economists / Econometrics (with Machine Learning) in Python
• Alfred Galichon - 'math+econ+code' masterclasses
• NBER - Spring 2022 heterogeneous-agent macro workshop

Python - Other Tutorials

• Research Software Engineering with Python: Building software that makes research possible
• 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
• Nicolas P. Rougier - From Python to Numpy
• Introduction to Mathematical Computing with Python and Jupyter
• Introduction to Data Science and Programming
• Cliburn - Computational Statistics and Statistical Computing
• The Hitchhiker's Guide to Data Science for Social Good

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 / Dynamic Optimization)
• Tyler Ransom - Econometrics III (structural estimation, machine learning) 2020, 2021
• 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
• Lutz Hendricks - Julia for Economists / Notes on the Julia Programming Language
• Jamie Cross - Introduction to Bayesian Econometrics
• Cameron Pfiffer - Julia for Economists Bootcamp
• Sergio Ocampo - Advanced Macroeconomics II

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
• From zero to Julia!
• Introduction to Scientific Computing
• Julia language: a concise tutorial
• Programming of Simulation, Analysis, and Learning Systems
• Julia Data Science
• Introduction to Datascience: Learn Julia Programming, Math & Datascience from Scratch.
• Scientific Programming in Julia / Julia for Optimization and Learning

R - Econ Lectures

• Maximilian Kasy - Advanced Econometrics and Machine Learning: 2019, 2020, 2021, 2023 / collection of useful computation links on R and ML
• Nick Huntington-Klein - Economics, Causality, and Analytics / R resources / Causal Inference / DataCommSlides
• 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 2020, 2022 / Masters-level applied econometrics with R / Introduction to Econometrics with R
• Tyler Ransom - Econometric Analysis (U)
• Hans H. Sievertsen - Applied Economics with R / Introduction to R
• Matt Woerman - Topics in Advanced Econometrics
• Sebastian Kranz - Empirical Economics with R / Market Analysis with Econometrics and Machine Learning
• Florian Oswald, et al. - Introduction to Econometrics with R / Advanced Econometrics
• David Childers - Econometrics II (U) / Causal Econometrics
• Nick Hagerty - Advanced Data Analytics in Economics

R - Other Tutorials

• Garrett Grolemund & Hadley Wickham - R for Data Science (2ed)
• 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
• Pere A. Taberner - List of open source books about R (resources)
• Kyle Walker - Analyzing US Census Data: Methods, Maps, and Models in R
• Summer Institutes in Computational Social Science
• Jenny Bryan - Data wrangling, exploration, and analysis with R

Machine Learning - Econ Lectures

• 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 - Prediction and machine-learning in econometrics 2020 2023 (R)
• Itamar Caspi - 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)
• Tyler Folkman - Applied Machine Learning (Python)
• Kathy Baylis et al. - Machine learning in applied economics 2020, 2021 (Python)
• Dawie van Lill - Data Science for Economics (Python)
• Economics and Data Science (Resources)
• Jonathan Hersh - Introduction to Machine Learning (R)
• Melissa Dell - Unleashing Novel Data at Scale (Python)
• Susan Athey's video on youtube / Machine Learning & Causal Inference: A Short Course
• Macroeconomic Analysis with Machine Learning and Big Data
• Jesús Fernández-Villaverde - Machine Learning for Macroeconomics (slide; video)
• Simon Scheidegger - Advanced Methods in Computational Economics

Machine Learning - Other Tutorials

• Chris Albon - Data Science & Machine Learning (Python)
• Andreas C. Müller - Applied Machine Learning Spring 2020 (Python)
• Jeff Heaton - Applications of Deep Neural Networks (Python)
• UCB - Computational Social Science (Python & R)
• Harvard - Introduction to Data Science (Python)
• Bradley Boehmke, Brandon Greenwell - Hands-On Machine Learning with R (R)
• Bloomberg - Foundations of Machine Learning
• fast.ai: Deep Learning / Practical Data Ethics / Computational Linear Algebra / NLP (Python)

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
• The Stata Guide on Medium / Stata-schemes - Asjad Naqvi
• Germán Rodríguez - Stata + R + GLMs + Multilevel + Survival + Demography
• Stata instructions for research projects
• Stata tips
• Top 25 Stata Visualizations With Full Code

Bayesian Statistics

• Richard McElreath - Statistical Rethinking: A Bayesian Course with Examples in R and Stan (& PyMC3 & brms & Julia)
• Aki Vehtari - Bayesian Data Analysis course (R)
• Robert Tucci - Bayesuvius: a visual dictionary of Bayesian Networks and Causal Inference

Basic CS Lectures

• The Missing Semester of Your CS Education
• Software Carpentry
• Harvard CS50
• NormConf: The NormCore Tech Conference
• MIT: Introduction To Computer Science And Programming / Introduction To Computer Science And Programming In Python / Software Construction / Introduction To Computational Thinking And Data Science

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
• LaTeX Table Hints and Tips
• Modern LaTeX
• Latex templates (Slide & Article) - Kyle Butts
• LaTeX Usage Notes
• annotated_latex_equations
• Draw DAGs with TikZ
• For Beautiful Presentations - Use PowerPoint BEAMER
• A LaTeX Power Up / A Basic Beamer Power Up - Natalia Emanuel
• latex-math tips / latex-paper template (explanation) / [latex-slide template](LaTeX template for academic presentations) (explanation) - Pascal Michaillat
• Beamer theme: Jambro Beamer theme

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)
• AutoRegex

Visualization

• Schwabish, J. A. (2014). An economist's guide to visualizing data. Journal of Economic Perspectives, 28(1), 209-34.
• Nicolas P. Rougier - Scientific Visualization – Python & Matplotlib / Scientific visualization Course / Matplotlib cheat sheet
• 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
• Claus O. Wilke - Fundamentals of Data Visualization

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
• The Programming Historian (lessons on digital tools, methods, and research processes)
• GDSGE: A Toolbox for Solving DSGE Models with Global Methods (matlab)
• Course and program information for Agent-Based Computational Economics (ACE), Agent-Based Modeling (ABM), and related topics
• Economics Simulations (an interactive educational application developed to simulate and visualize various statistical concepts)
• 10 Fundamental Theorems for Econometrics
• Numerical Tours of Data Sciences in Matlab, Python and Julia
• Introduction to Modern Causal Inference

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
• Dietz Vollrath - (UG) Growth
• Ömer Özak - Economic Growth and Comparative Development / Macroeconomics II
• Jonathan Dingel - International Macroeconomics and Trade
• David Ubilava - (UG) Agricultural Markets
• Ivan Rudik - Environmental and Resource Economics
• José Luis Montiel Olea - IntroEconometrics-Ph.D / TimeSeries-UG
• Jonathan Conning - Development Microeconomics / (UG) International Trade
• Maximilian Kasy - Labor Economics (UG) / Empirical research on economic inequality
• Florian Oswald - graduate labor (2018)
• Thibaut Lamadon - Micro-econometric methods (UG) / Big data tools for economics (UG) / Topics in Labor (PhD)

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 / Professional Development Resources
• Mizuhito Suzuki - Resources
• Christoph Kronenberg - Resources
• Christine Cai - Public Goods
• Brendan Price - Resources
• Alex Albright - (mainly R) Resources
• Claes Bäckman - Resources