Public goods for graduate students in economics

Although there are a number of websites that provide useful free resources for graduate students in economics, most are out of date and/or don't include online courses and books. This post is an attempt to fill that gap. A ✨ indicates a particularly good resource on a topic. Enjoy!

Courses and Books#

Below are some online courses and books that focus mainly on programming for economists, causal inference, machine learning and economic theory. I have divided the courses into these categories for a better overview, but they may contain parts of other categories as well.

Programming for Economists#

• Coding for Economists by Arthur Turrell ✨
• R for Data Science by Hadley Wickham, Mine Çetinkaya-Rundel, and Garrett Grolemund
  • see also: Big Book of R, a collection of various R-coding books
• QuantEcon lectures by Thomas J. Sargent and John Stachurski
  • Also includes PhD-level economic modeling
• Building Reproducible Analytical Pipelines by Bruno Rodrigues

Causal Inference#

• The Effect: An Introduction to Research Design and Causality by Nick Huntington-Klein ✨
• Causal Inference: The Mixtape by Scott Cunningham
  • Hard fork of mixtape-learnr repo (improved R-code) by Grant McDermott
• Library of Statistical Techniques (LOST) by Nick Huntington-Klein and contributors
  • Tidy resource on how to implement various statistical techniques in programming languages (mostly Python & R)
• Causal Inference for The Brave and True by Matheus Facure Alves
• Model to Meaning: How to Interpret Statistical Models by Vincent Arel-Bundock
• Time Series Analysis: Lecture Notes with Examples in R by Vyacheslav Lyubchich and Yulia R. Gel
  • Also has a section on forecasting
• Time Series Econometrics and Economic and Business Forecasting by Jan Duras
• Understanding Bayes by Alexander Etz
• Econometrics with Unobserved Heterogeneity by Vladislav Morozov
• Difference-in-Differences Designs: A Practitioner's Guide by Andrew Baker, Brantly Callaway, Scott Cunningham, Andrew Goodman-Bacon, and Pedro H. C. Sant'Anna

Machine Learning#

• Deep Learning for Economists by Melissa Dell ✨
  • Associated paper
• Fast data science: practical introduction to Python, machine learning methods and Statistics by Marco Inacio
  • Also includes a basic introduction to Python
• Neural Networks: Zero to Hero by Andrej Karpathy

Economic theory#

• Macroeconomics: A comprehensive textbook for first-year Ph.D. courses in macroeconomics by M. Azzimonti, P. Krusell, A. McKay, and T. Mukoyama
  • And also, for students at the Master level: Advanced Macroeconomics: An Easy Guide by Filipe Campante, Federico Sturzenegger, Andrés Velasco
  • For the actual literature: Reading List in Macroeconomics and Monetary Economics (a bit outdated)
• Dynamic Programming by Thomas J. Sargent and John Stachurski
  • corresponding webpage

PhD resources#

Some resources that are especially relevant for (economics) PhD students. You can find more information on, e.g., general grad-school advice on EconGradAdvice or on the collection by Claes Bäckman (see Other resource collections).

Writing#

• Writing Tips for Crafting Effective Economics Research Papers – 2023-2024 Edition by Plamen Nikolov
• Learnings From 1,000 Rejections by Alex Edmans
• Writing Tips for PhD Students by John Cochrane
• Writing matters by Jan Feld, Corinna Lines, and Libby Ross
• How to publish in top journals by Kwan Choi
• DeepL Write (or, of course, tools like ChatGPT, Claude etc.)

Presentations#

(some parts shamelessly stolen from Wouter Den Haan)

• Beamer tips by Paul Goldsmith-Pinkham ✨
• How to avoid death By PowerPoint by David JP Phillips
• Tips on how to avoid disaster in presentations by Monika Piazzesi
• Tips on Preparing for (a) Workshop by Tim Kehoe (updated by Franck Portier)
• Tips on presentations by Eric Sims
• Presentation tips by Francesco Caselli; Longer version by David Weinstein
• Rules for Presentations by Matthias Doepke
• Presentation tips by Jesse Shapiro

Beamer templates#

It is notoriously hard to find good and modern-looking Beamer templates. There are a few exceptions though:

• Metropolis
  • Good-looking font and overall very minimalistic
• Argüelles
  • Has a nice progress-bar on top, a bit less minimalistic
  • Also has a sans-serif option
• Elegant Slides
  • My own version with a serif font: Elegant Slides with a serif font

Graphs#

• Data Visualization: A practical introduction by Kieran Healy ✨
• The R Graph Gallery
  • Good starting point for graphs with R/ggplot2
• The Python Graph Gallery
  • Same as above, but for Python/matplotlib
• Everything by John Burn-Murdoch:
  • Making charts that make an impact
  • This Bluesky thread by Evan Peck
  • This interview with datawrapper
• Let's Plot - ggplot2 inspired plotting in Python

Other#

• The Backwards Induction Approach to Grad School... and other random advice by Paul J. Healy ✨
• The Econ PhD placements dataset by Pablo García Guzmán ✨
• The Hidden Curriculum podcast
• Nine facts about top journals in economics (Paper, 2018 Update) by Stefano DellaVigna and David Card
• Models, Measurement, and the Language of Empirical Economics by Phil Haile
  • Explains the key terminology surrounding Empirical Economics
• The Research Paper Playbook: A PhD Student's Guide to Writing and Presenting by Omri Even-Tov and Kristen Valentine

Newsletters#

Get the newest research straight to your inbox!

• NBER Working Papers (New This Week)
• IZA Discussion Papers (RIP 🪦)
• RePEc IDEAS MyIDEAS Weekly Digest
  • Follow authors, journals or topics and receive weekly E-Mails with the newest research

Some other, economics-related, blogs/newsletters:

• Economic Forces
  • Nerdy Newsletter on Price Theory
• Apricitas Economics
  • Explaining Macro developments with fancy graphs
• Data Colada
  • Academic replication blog
• Stay-at-Home (SAHM) Macro by Claudia Sahm
• Liberty Street Economics from the New York Fed
  • Macro policy research blog

Data#

• Global Macro Database by Karsten Müller, Chenzi Xu, Mohamed Lehbib, and Ziliang Chen ✨
  • Harmonized macro dataset covering 46 variables
  • See also the Macrohistory Database by Òscar Jordà, Moritz Schularick, and Alan M. Taylor
• Data for students by Jonas Vlachos ✨
  • Collection of datasets with a focus on publicly available microdata
• Analyze Survey Data for Free by Anthony Damico
  • "Forty-Nine Public Microdatasets, One Easy To Type Website"
• Datasets by Nicholas Decker
• Links to (German) Data by Alexander Busch
  • He also links to European Data Sources
• A Guide to Regional Data in Europe by Moritz Marbach
  • Similar: A Guide to Germany's Regional Data by Moritz Marbach
• Find Economic Articles with Data by Sebastian Kranz
• A review of APIs that may be useful for social scientists

Other#

• Generative AI for Economic Research project by Anton Korinek, regularly updated
• Translating Stata to R
• Tables Generator
  • never generate another $\LaTeX$ table by hand again!
• Minimalist LaTeX Template for Academic Papers and LaTeX Commands to Write Math (in Economics) by Pascal Michaillat
• EconGraphs: Interactive graphs and explanations about key economic concepts for use in teaching and exploring
• Empirical Studies of Conflict (ESOC) pre-doc training by Alicia Chen and Samikshya Siwakoti
  • Extensive materials for a bootcamp covering everything surrounding data analysis
• Economics Literature Search by Paul Goldsmith-Pinkham
• Teach SQL in (grad) school by Alessandro T.-A. Martinello
  • See also (Pretty) big data wrangling with DuckDB and Polars by Grant McDermott
• Resolve by Michel Nivard
  • Overleaf-type collaboration for .ipynb
• Introduction to Julia for R users by Nicola Rennie
• Why I no longer recommend Julia by Yuri Vishnevsky
  • And the response: Why I still recommend Julia (for Data Science) by Rik Huijzer
• econimate YouTube channel
  • Animates econ papers!

Other resource collections#

• Collection by Christine Cai ✨
• Collection by Claes Bäckman ✨
• EconGradAdvice by Chris Roth and David Schindler
  • The most comprehensive collection I've seen so far, with a lot of resources around the organizational part of grad school
• Useful computational resources by Maximilian Kasy
• Collection by Michèle Tertilt
• Collection by Wouter Den Haan