Spatial Regression (SAR, SEM, SDM)

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

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)

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