Estimate the within-country dynamic effect of war on log GDP per capita using Arellano-Bond GMM in Stata, reproducing Thies and Baum (2020) on a 1955-2015 panel of 160 countries.
Learn Difference-in-Differences (DiD) in Python using PyFixest and Great Tables. Covers the 2x2 design, TWFE regression, inference comparison, publication-quality tables, event studies, and parallel trends testing based on Corral and Yang (2024).
Replicate Hodler and Raschky (2014) to estimate the causal effect of economic shocks on civil conflict using 2SLS instrumental variables with panel data from 5,689 African regions
Estimate the causal effect of California's Proposition 99 tobacco control program on cigarette sales using the synthetic control method in Stata, with in-space placebo, in-time placebo, and leave-one-out robustness tests
Learn Difference-in-Differences (DiD) in Stata using a case study of an after-school tutoring program. Covers the 2x2 design, TWFE regression, event studies, and parallel trends testing based on Corral and Yang (2024).
Evaluate the causal effect of a school tutoring program on student exit exam scores using sharp regression discontinuity design with parametric OLS and nonparametric rdrobust estimation in Stata
Estimate spatial dynamic panel models with unobserved common factors using the spxtivdfreg package in Stata --- an IV approach that handles spatial lags, temporal persistence, endogenous regressors, and latent factors simultaneously
A hands-on guide to the fwlplot package in R --- from understanding the Frisch-Waugh-Lovell theorem through simulated confounding to visualizing fixed effects in real panel data --- showing what "controlling for" looks like as a scatter plot.
A hands-on guide to the scatterfit package in Stata --- from understanding the Frisch-Waugh-Lovell theorem through simulated confounding to visualizing fixed effects in real panel data --- showing what "controlling for" looks like as a scatter plot.