Student resources

The resources I wish I had known about a decade ago.

One groundrule: Do the work. Always read the law. Always read the footnotes. Always read about the assumptions of the model. How to read an article: “The Butler Did It”

Selected public policy topics

“Why does DARPA work?” by Ben Reinhardt
“SLS: Is cancellation too good?”
A Policymaker’s Guide to the Longevity Therapeutics Industry
CRS Report “The National Science Foundation: An Overview”

Economic lectures + notes

Price Theory in Economics
Intermediate Micro Review
Notes on Macroeconomic Theory by Steve Williamson
Discrete-Choice Models of Demand
Decision Theory: A Brief Introduction [source]
Regional Impact Models by William A. Schaffer; A critique of input-output models
Market Efficiency and Market Failures by Jon Steinsson; The concept of efficiency in economics
Profit maximization
Intermediate Macroeconomics: Consumption Eric Sims
Options markets
Important Papers for Quantitative Traders
Public finance notes

Classic papers

Hirshleifer on Private and Social Value of Information
“The Economics of Information” (1961) by George J. Stigler
“An Introduction to Privacy in Economics and Politics” (1980) by George J. Stigler
Stigler (“The Economics of Information”)
Ozga (“Imperfect Markets through Lack of Knowledge”)
Arrow (“Economic Welfare and the Allocation of Resources for Invention”)
Demsetz (“Information and Efficiency: Another Viewpoint”)
Alchian (“Information Costs, Pricing, and Resource Unemployment”)

The seen and the unseen

Dissoi Logoi is a rhetorical exercise of unknown authorship, most likely dating to just after the Peloponnesian War (431–404 BC) based on comments within the exercise’s text.

The limits of knowledge in machine learning

“A Few Useful Things to Know about Machine Learning” by Pedro Domingos (2012) alternate version
“Data mining fool’s gold” by Gary Smith

Discounting and time

“Sustainability: An Economist’s Perspective” by Robort Solow
“On hyperbolic discounting and uncertain hazard rates”
“Searching for Safety”
RISK IN TIME: The Intertwined Nature of Risk Taking and Time Discounting

Information and firms

“Designing organizations for an information-rich world” by Herbert Simon
“The Dynamo and the Computer: An Historical Perspective on the Modern Productivity Paradox”

Economics of property rights

“Some economics of property rights” Armen Alchian

Economics of AI

The New Business of AI (and How It’s Different From Traditional Software) by Martin Casado and Matt Bornstein
“Taming the Tail: Adventures in Improving AI Economics” by Martin Casado and Matt Bornstein

Robots and workers

“Are Workers Losing to Robots?” By Sylvain Leduc and Zheng Liu
“Measuring the Gig Economy: Current Knowledge and Open Issues” by Katharine G. Abraham, John C. Haltiwanger, Kristin Sandusky & James R. Spletzer

Zero priced goods

“The Specialness of Zero” by Joshua Gans (2020)
“The Economics of Information” (1961) by George J. Stigler

Platform economics

“The Economics of Two-Sided Markets” Marc Rysman (2009)
“Controllability of complex networks” by Yang-Yu Liu, Jean-Jacques Slotine & Albert-László Barabási (2011)

Public policy and wicked problems

“Dilemmas in a general theory of planning” by Horst W. J. Rittel & Melvin M. Webber

DARPA

“The DARPA Model for Transformative Technologies: Perspectives on the U.S. Defense Advanced Research Projects Agency”
“Why does DARPA work?” by Ben Reinhardt

NASA and space

“SLS: Is cancellation too good?”

The economics of privacy

“The Economics of Privacy” (1981) by Richard A. Posner
“Privacy Regulation and Online Advertising” by Avi Goldfarb and Catherine E. Tucker
“Privacy Regulation and Market Structure” (2011) by James Campbell, Avi Goldfarb, and Catherine Tucker

Freedom of speech

“Freedom of Speech, Information Privacy, and the Troubling Implications of a Right to Stop People from Speaking About You” (1999) by Eugene Volokh

Theory of computer science

Public economics + public finance

Lists of biases:

https://thedecisionlab.com/biases

Literature reviews

Technology and jobs: A systematic literature review

Reading lists

“Reading List in Macroeconomics and Monetary Economics” (2014)
“New Institutional Economics: Reading List, Introductory”
“Democracy in a Digital Age” Papacharissi
“Seminar in Media Studies” Papacharissi

Math + stats:

“Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations.” (link)
If you have technical questions, check out Luke Stein’s Stanford graduate economics core. 65 pages of stats review.
Power Laws in Economics: An Introduction
A list of great math videos online.
NIST/SEMATECH e-Handbook of Statistical Methods
Rob Hyndman’s Forecasting Principles and Practice (FPP3) book is a great resource that covers both the math and applications to real data.
Machine Learning Mastery
Bayesian statistics with R
Statistical Rethinking: A Bayesian Course (with Code Examples in R/Stan/Python/Julia)
YouTube series: Statistical Rethinking Winter 2019
An explainer on the problem of bias.
The notes and problems from Yales Math Camp.
Some Linear Algebra for Econometrics [pdf]
Essentials of Stochastic Processes by Rick Durrett

Econometrics + model making

“This document contains the set of lecture notes from the late Gary Chamberlain’s 2010 Econometrics class (EC2120) that I (Paul Goldsmith-Pinkham) took during my economics Ph.D. at Harvard University. Gary was a remarkable teacher and this class was an amazing experience for me as a young economist.” [Github]
Cara Jackson is collecting Wooldridge’s Twitter lessons in a Google Doc.

Lecture Notes on Identification Strategies by Štˇepán Jurajda [source]
Quick review of OLS assumptions [itfeature]
Quick-R: Regression Diagnostics
Quick-R: Multiple Regression
When Should I Use Confidence Intervals, Prediction Intervals, and Tolerance Intervals [Minitab]
Logistic & Tobit Regression [pdf]
Affine term structure models [pdf]
When to include interaction terms in your regression.
Frank Pinter has notes on
- Demand Estimation Notes [pdf]
- Git: A Guide for Economists [pdf]

From Christine Cai

Metrics discussions: diff-in-diff and event studies (by Chloe East)
DiD Reading Group (Summer 2021) (by Taylor Wright)
Current literature on diff-in-diff (by Asjad Naqvi)
Course videos and slides: Applied Empirical Methods (by Paul Goldsmith-Pinkham)
Course notes from classes at UC Berkeley (by Kristy Kim)
Graduate Public Finance lecture materials (by Owen Zidar)
Graduate Labor Econ course materials (by David Autor)
Graduate (Macro) Labor Econ course materials (by Florian Oswald)
Gary Chamberlain’s lecture notes (by Paul Goldsmith-Pinkham)
Graduate Development & Econometrics lecture notes (by Simon Quinn)
The Stata workflow guide (by Asjad Naqvi)
Coding and do-file structure (by Michael Stepner)
Stata coding guide (by Julian Reif)
Stata cheat sheets (by Tim Essam & Laura Hughes)
Customizing Stata graphs (by Ben Jann)
Maps in Stata (by Asjad Naqvi)
Advanced Mapping with Stata: OpenStreetMap (OSM) and QGIS (by Asjad Naqvi)
Stata guide on Mata (by Asjad Naqvi)
Stata guide on MLE (by Asjad Naqvi)
Microeconometrics Using Stata (by Colin Cameron & Pravin Trivedi)
Tips for managing large-scale datasets efficiently in Stata
Introduction to R (by Hans H. Sievertsen)
Applied Economics with R (by Hans H. Sievertsen)
Regression analysis in R (by Grant R. McDermott)
List of open source books about R (by Pere A. Taberner)
Big Book of R
LaTeX Table Hints and Tips
Statistical models across R/Python/Stata (by the Library of Statistical Techniques)
Stata-to-Python equivalents (by Daniel M. Sullivan)
StataTex Blog: Tips for Stata, Latex and other useful resources for applied economists (by Jan Sauermann)
Stata and GitHub Integration (by Asjad Naqvi)
Conducting power calculations in Stata/R (by Sabhya Gupta)

Programming for economists, R, python, libraries, packages, etc.

Stock-flow Consistent Macroeconomic Models: A Survey | Levy Economics Institute
The FRBNY DSGE Model Meets Julia Liberty Street Economics
PyIO: Python Module for Input-Output Analysis
QuantEcon
The Causal Analytics Toolkit (CAT) provides powerful, easy-to-use software to help overcome these limitations. It makes state-of-the-art causal analytics available to anyone who has Microsoft Excel.
How to run specification curve analysis in R. R packages of note:
“Start with R for Data Science. Efficient R Programming also helps a lot. If you’re doing computational stuff specifically, though, don’t use R. Here’s how with Python or Julia. Instead of Anaconda, I’d recommend Poetry. No, pip alone won’t cut it. This is a very good comment but I’d like to add that poetry doesn’t let you use different python versions like Anaconda does and you’ll have to manage it with yet another tool like pyenv.”
The estimatr package (https://cran.r-project.org/packages/estimatr) will do 80%+ of the regressions you need for day-to-day micro work. In ~1 line.
lfe package (https://CRAN.R-project.org/package=lfe) is also super. Four dimensions of fixed effects, multi-way clustered SEs, no problemo.
Need marginal effects estimates? @thosjleeper got ya covered with the margins package (https://cran.r-project.org/package=margins)
All sorts of machine learning tools. New stuff all the time. https://cran.r-project.org/web/views/MachineLearning.html
Read about other tools here: https://cran.r-project.org/web/views/Econometrics.html
Another example: Competing ML packages all have different syntax, different option sets, different output structure. @topepos’s parsnip package https://tidymodels.github.io/parsnip/) and the tidymodels infrastructure are working on improving things though.
The MatchIt and WeightIt (https://cran.r-project.org/web/packages/WeightIt…) packages aim to do similar things for propensity score analysis.
pyEdgeworthBox provides with a tool to plot the Edgeworth box and calculate equilibrium, core, pareto effective allocation etc in the pure exchange economy. [Github]
cspp is a package designed to allow a user with only basic knowledge of R to find variables on state politics and policy, create and export datasets from these variables, subset the datasets by states and years, create map visualizations, and export citations to common file formats (e.g., .bib). [Github]
NeuralNetworkLawfirmNames – Using torch, generated list of law firms, trained using Law360’s Top 400 law firms by size [Github]
Python word2vec [link]
GeoDa [link]
Using PGFPLOTS to make economic graphs in LATEX [link]
Bayesian histograms for rare event classification