Robust Standard Errors for Panel Regressions with Cross-Sectional Dependence

TLDR

The study proposes a Hausman‑type test for fixed effects that is robust to general cross‑sectional and temporal dependence. The authors introduce the Stata program xtscc, which estimates pooled and fixed‑effects regression models with Driscoll–Kraay standard errors, and evaluate its finite‑sample properties via Monte Carlo simulations, illustrating its use with an empirical finance application. Monte Carlo results show that Driscoll–Kraay standard errors are well calibrated when cross‑sectional dependence is present, whereas ignoring such dependence can produce severely biased statistical conclusions.

Abstract

I present a new Stata program, xtscc, that estimates pooled ordinary least-squares/weighted least-squares regression and fixed-effects (within) regression models with Driscoll and Kraay (Review of Economics and Statistics 80: 549–560) standard errors. By running Monte Carlo simulations, I compare the finite-sample properties of the cross-sectional dependence–consistent Driscoll–Kraay estimator with the properties of other, more commonly used covariance matrix estimators that do not account for cross-sectional dependence. The results indicate that Driscoll–Kraay standard errors are well calibrated when cross-sectional dependence is present. However, erroneously ignoring cross-sectional correlation in the estimation of panel models can lead to severely biased statistical results. I illustrate the xtscc program by considering an application from empirical finance. Thereby, I also propose a Hausman-type test for fixed effects that is robust to general forms of cross-sectional and temporal dependence.