Resources for Economists
"The pursuit of excellence does not need justification."
In this page I list some resources I collected over time that I think are useful for Economists, in particular if you work either in Macroeconomics or more technical Finance questions. This page reflects 100% my own personal biases.
Use/read/cite at your own discretion.
AI-Related Stuff
AI is here to stay (and might take our jobs). May the force be with us.
- Intro to Claude Code for Academics: I personally lean Codex, but the concepts translate back and forth.
- Claude Code Workflow by Pedro Sant’Anna: great if you’re new to
skillsand agents. - VS Code Live: Agent Sessions: the VS Code team on customizing agents. I’m a BIG fan of VS Code and Copilot.
- Awesome Copilot: community prompts and agents for customizing Copilot.
Life in the Fast (Terminal) Lane
Back at Northwestern, Bob McDonald once told me “I live in the Terminal!”. Sounded cool, so I try to keep up. Tools that make the ride smoother:
- tmux: keeps sessions, windows, and panes alive so one closed window doesn’t ruin your day. I live between office and home on a remote server, so this is essential.
- Starship: a fast, customizable prompt that warns you before you run the wrong command in the wrong folder. Great
condasupport too. - Oh My Zsh: themes, plugins, autocomplete. The terminal can have nice things too.
- Homebrew: the package manager you’ll want five minutes after setting up a Mac.
- Ghostty: fast, polished, native terminal emulator. A millionaire’s passion project – expect great stuff.
- Glow: a terminal Markdown reader. Fast and nice, and Markdown is the language of AI now.
Coding Like The Old Days
I like writing code, and do it better every day (less true since agents took over…). Some general principles apply across the board, IMHO.
- Classes by Jesus Fernandez-Villaverde: see Computational Methods for Economists. Useful even if you don’t do Macro.
- The Missing Semester of Your Compute Science Education: MIT lectures on terminals, version control, editors – useful regardless of field. I revisit this from time to time.
- Combining DropBox or GitHub: a tutorial for the tricky bit of pairing Git/GitHub with DropBox (or Drive, Box, etc.) for large files.
- Data Science for Economists: Grant McDermott’s (Oregon) class on terminal and Git basics. R-centric but mostly language-agnostic.
- Python and R Guides by Sean Higgins: Sean (Kellogg) on empirical methods and managing large collaborative projects, in Python, R, and Stata.
- Code and Data, by Matthew Gentzkow and Jesse Shapiro: older but timeless. Brandon Zborowski says it’s mandatory reading at the NY Fed’s RA program and in Econ consulting.
- The Good Research Code Handbook, by Patrick Mineault: organizing projects, documentation, modules. Python-centric, but great regardless. Thanks to Nate Lawrence for the pointer!
Data Resources
Sometimes finding data is hard. Remember to correctly cite the providers if you use their data.
- Open Source Asset Pricing: 300+ monthly “anomaly” factor series, with a companion paper.
- Saketh Aleti’s High Frequency Data: free 1-minute returns for Asset Pricing factors. Saketh’s a great guy, though this doesn’t look actively updated anymore.
- NEFIN @ USP: a Finance research center at USP computing indicators for the Brazilian market, including the Fama-French 5 factors.
Statistical Methods in Finance and Economics
Material at the intersection of Machine Learning and Economics/Finance.
- A Similar Page by Dario Sansone: Dario (Exeter) curates papers and classes on ML, mostly through an Econ lens. Super detailed.
- Financial Machine Learning: Kelly and Xiu’s review, focused on prediction. See Fernandez-Villaverde above for the “ML to solve models” angle instead.
- Deep Learning for Economists: Melissa Dell’s great intro – a good place to start. More of her teaching at EconDL.