Robust standard errors for panel regressions with cross–sectional dependence
Daniel Hoechle
Department of Finance
University of Basel
Basel, Switzerland
[email protected]
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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 nance. Thereby, I also propose
a Hausman-type test for fixed effects that is robust to general forms of
cross-sectional and temporal dependence.
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Daniel Hoechle
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xtscc, robust standard errors, nonparametric covariance estimation
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