Identify latent group structures in panel data using the Classifier-LASSO method (Su, Shi, Phillips 2016), revealing that the pooled democracy-growth effect of +1.055 masks a +2.151 effect in 57 countries and a -0.936 effect in 41 countries.
Manual demeaning vs two-way fixed effects --- showing that TWFE is just OLS on demeaned data through the Frisch-Waugh-Lovell theorem, with a hands-on proof using a Barro convergence panel of 150 countries.
Comparing standard error estimators in panel data regressions using Python and linearmodels --- from conventional to clustered, Driscoll-Kraay, and fixed effects
Dynamic panel Bayesian Model Averaging with the Bayesian Dynamic Systems Modeling (BDSM) R package, applied to cross-country economic growth determinants --- handling reverse causality through lagged dependent variables, fixed effects, and weak exogeneity.
Bayesian Model Averaging and Double-Selection LASSO applied to the Environmental Kuznets Curve using synthetic panel data with a known answer key, demonstrating how both methods recover the true predictors of CO2 emissions.
A hands-on guide to spatial panel data modeling using the SDPDmod package in R --- from Bayesian model comparison through static and dynamic SAR/SDM estimation with Lee-Yu bias correction to direct, indirect, and total effect decomposition --- applied to cigarette demand across 46 US states (1963--1992).
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.
Evaluate the causal effect of a cash transfer program on household consumption using regression adjustment, inverse probability weighting, doubly robust, and difference-in-differences methods in Stata