fixed effects

Introduction to Panel Data Methods in Python

Does joining a union raise wages? Following the same workers over two years in Python, we compare seven ways to analyze repeated data and see that the estimated wage gain nearly triples once each worker is compared with the same worker at another time. It comes with an interactive app that runs in your web browser.

Spatial Inequality and the Kuznets Curve: Parametric and Semiparametric Estimates in R

Do gaps between rich and poor regions within a country first widen and then narrow as the country develops? Using simulated data for 56 countries that mirrors a published study, we measure regional inequality and trace how it rises and falls with income, from simple to more flexible methods. This R tutorial comes with an interactive app that runs in your web browser.

MGWFER: Causal Spatially Varying Coefficients via Panel Fixed Effects

When we measure how a relationship changes from place to place, hidden features of each place can distort the answer. This Python tutorial follows a 2026 study by Li and Fotheringham and uses simulated data on 225 places over three periods to show how following the same places over time removes that distortion. It comes with an interactive app that runs in your web browser.

What Does TWFE Actually Do? Manual Demeaning and the FWL Theorem

What does a regression that adjusts for country and year differences actually do to the data? Using simulated data for 150 countries in R, we show step by step that it equals subtracting country and year averages before a simple regression, and why the usual margins of error then need a fix. It comes with an interactive app that runs in your web browser.