Posts & Tutorials

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 …

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 …

Comparing standard error estimators in panel data regressions using Python and linearmodels --- from conventional to clustered, Driscoll-Kraay, and fixed effects

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 …

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 …

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 …

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 …

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, …

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, …

Assess how robust difference-in-differences results are to violations of parallel trends using the honestdid package in Stata, progressing from a simple 2x2 DiD to multi-period event studies with …

A guide to Difference-in-Differences with staggered treatment --- from TWFE pitfalls through Callaway-Sant'Anna group-time ATTs, doubly robust estimation, and HonestDiD sensitivity analysis --- …

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

Three principled approaches to variable selection---BMA, LASSO, and WALS---applied to synthetic cross-country CO2 emissions data with known ground truth, demonstrating methodological triangulation for …

Synthetic control with prediction intervals quantifies uncertainty in Germany's reunification GDP impact using the scpi package.

Applying Multiscale Geographically Weighted Regression (MGWR) to reveal how economic catching-up varies across Indonesia's 514 districts, with each variable operating at its own spatial scale

An introduction to exploratory spatial data analysis using PySAL, covering choropleth maps, spatial weights, Moran's I, LISA clusters, space-time dynamics, and a Venezuela-Bolivia comparative analysis …

Building a comparable Human Development Index across two time periods using pooled PCA with real sub-national data for 153 South American regions, and contrasting with per-period PCA to show why …

Building a composite Health Index from Life Expectancy and Infant Mortality using manual PCA with simulated data for 50 countries, then verifying against scikit-learn

Estimating regression models with high-dimensional fixed effects using PyFixest, from simple OLS through two-way FE, instrumental variables, panel data, and event studies

Estimating causal treatment effects using Difference-in-Differences with the diff-diff package, from the classic 2x2 design through staggered adoption with Callaway-Sant'Anna and HonestDiD sensitivity …

Understanding the Frisch-Waugh-Lovell theorem to isolate causal relationships by partialling-out confounders in a simulated retail store dataset

Computing causal bounds under unmeasured confounding using Manski and Tian-Pearl bounds with the CausalBoundingEngine package in Python

Estimating the causal effect of a job training program on earnings using DoWhy's four-step causal inference framework with the Lalonde dataset

Predicting municipal development in Bolivia using Random Forest regression on satellite image embeddings

Estimating the causal effect of a cash bonus on unemployment duration using Double Machine Learning with the Pennsylvania Bonus Experiment

An interactive exploration of the space-time dynamics of mean luminosity using the VIIRS-like data over the 1992-2023 period.

An interactive exploration of the space-time dynamics of luminosity-based GDP over the 1992-2019 period.

An interactive exploration of the space-time dynamics of mean luminosity using the DMSP-like data over the 1992-2019 period.

An introduction to heterogeneous treatment effects using the two-stage DID estimator of Gardner (2021)

An interactive exploration of the space-time dynamics of nighttime lights using VIIRS-like data.

An interactive exploration of the space-time dynamics of nighttime lights using VIIRS-annual data.

An interactive exploration of the space-time dynamics of nighttime lights using DMSP-like data.

An interactive exploration of the space-time dynamics of nighttime lights using DMSP-extended data.

An interactive exploration of the space-time dynamics of nighttime lights using DMSP-corrected data.

An interactive geocomputational notebook to study spatial clusters and outliers

A geocomputational notebook to study spatial heterogeneity using the GWR and MGWR frameworks.

An introduction to how to construct, explore, and export spatial connectivity structures (W) using Python.

Model spatial spillovers in panel data using the Spatial Durbin Model (SDM), Wald specification tests, and dynamic extensions with the xsmle package in Stata

Explore the full taxonomy of cross-sectional spatial models --- OLS, SAR, SEM, SLX, SDM, SDEM, SAC, and GNS --- using the Columbus crime dataset in Stata, following Elhorst (2014)

A geocomputational notebook to monitor subnational human development using Python. Besides exploratory data analysis, the notebook introduces geospatial mapping, spatial dependence, and spatial …

An introduction to difference in differences with multiple time periods and staggered treatment adoption.

The book provides a succinct review of the recent club convergence literature, a comparative view of developed and developing countries, and a tutorial on how to implement the club convergence …

An introduction to difference in differences with multiple time periods and staggered treatment adoption.

A geocomputational notebook to monitor spatial inequality dynamics.

A geocomputational notebook to monitor regional development in Bolivia using Python. Besides exploratory data analysis, the notebook introduces geospatial mapping, spatial dependence, spatial …

An introduction to the Solow growth model and its convergence prediction

Theresa Graefe (Ulm University) has created a very nice RTutor that allows you to replicate the main results of a recent AEJ paper on the causal effects of a CO2 tax in Sweden using the syntetic …

Fitting and interpreting linear and logistic regression models

What is the relation between internet usage and democracy?

Measuring the evolution of spatial dependence and spatial inequality

This method constructs a synthetic control unit as a weighted average of available control units that best approximate the relevant characteristics of the treated unit prior to treatment.

An introduction to the basic differences in differences method using the classical incenerator example of Kiel and McClain (1995)