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Robust Standard Errors for Panel Regressions with Cross-Sectional Dependence

Robust Standard Errors for Panel Regressions with Cross-Sectional Dependence 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png "Stata Journal, The" SAGE

Robust Standard Errors for Panel Regressions with Cross-Sectional Dependence

"Stata Journal, The" , Volume 7 (3): 32 – Sep 1, 2007

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References (39)

Publisher
SAGE
Copyright
© 2007 StataCorp LLC
ISSN
1536-867X
eISSN
1536-8734
DOI
10.1177/1536867X0700700301
Publisher site
See Article on Publisher Site

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.

Journal

"Stata Journal, The"SAGE

Published: Sep 1, 2007

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