(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
- 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)
- Michael Keane - Practical Issues in Structural Estimation
- Chris Taber - Estimation of Policy Counterfactuals / Structural Estimation
- Tony Smith - Tips for Doing Computational Work in Economics
- Dynamic Structural Econometrics Summer School & Conference Series
- Robert A. Miller - STRUCTURAL ECONOMETRICS
- 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
- 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
- Awesome Causal Inference (resource)
(see inference for more)
- 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
- 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
- Geographic Data Science with Python
- 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
- 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
- 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
- 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
- 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) / Deep Learning for Economics
- 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
- Goutham Gopalakrishna - mini lecture on Deep Learning and Macro-Finance Models
- European Central Bank Machine Learning Training - Machine learning for econometricians (R)
- Applied Causal Inference Powered by ML and AI (Python; R)
- Zhigang Feng - Machine Learning for Quant Macro
- 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)
- Klaus-Robert Müller - Julia programming for ML (Julia)
- 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
- Quick Stata Tips
- Top 25 Stata Visualizations With Full Code
- 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
- 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
- pure bash bible
- useful bash scripts shared by economists: Ed Rubin, John Horton
- 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 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
- 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
PowerPointBEAMER - 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
- The Markdown Guide
- Markdown Reference - typora (some Japanese introduction on typora)
- Learn regex the easy way
- Online regex tester and debugger (also this)
- AutoRegex
- AI-Powered Regular Expression Solver
- 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
- Friends Don't Let Friends Make Bad Graphs (R)
- (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
- 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)
- 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
- Advice for Phd Students in Economics