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Robust standard errors for panel regressions with cross-sectional dependence

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  • Daniel Hoechle

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    (Department of Finance, University of Basel)

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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 Driscol-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. Copyright 2007 by StataCorp LP.

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    Bibliographic Info

    Article provided by StataCorp LP in its journal Stata Journal.

    Volume (Year): 7 (2007)
    Issue (Month): 3 (September)
    Pages: 281-312

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    Handle: RePEc:tsj:stataj:v:7:y:2007:i:3:p:281-312

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    Related research

    Keywords: xtscc; robust standard errors; nonparametric covariance estimation;

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