Sequences of bias-adjusted covariance matrix estimators under heteroskedasticity of unknown form |
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Authors: | Francisco Cribari-Neto Maria da Glória A Lima |
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Institution: | (1) Institute of Public Health, Health Economics, Campusvej 55, 5230 Odense M, Denmark;(2) Department of Economics, University of Kassel, 34109 Kassel, Germany |
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Abstract: | The linear regression model is commonly used by practitioners to model the relationship between the variable of interest and
a set of explanatory variables. The assumption that all error variances are the same, known as homoskedasticity, is oftentimes
violated when cross sectional data are used. Consistent standard errors for the ordinary least squares estimators of the regression
parameters can be computed following the approach proposed by White (Econometrica 48:817–838, 1980). Such standard errors,
however, are considerably biased in samples of typical sizes. An improved covariance matrix estimator was proposed by Qian
and Wang (J Stat Comput Simul 70:161–174, 2001). In this paper, we improve upon the Qian–Wang estimator by defining a sequence
of bias-adjusted estimators with increasing accuracy. The numerical results show that the Qian–Wang estimator is typically
much less biased than the estimator proposed by Halbert White and that our correction to the former can be quite effective
in small samples. Finally, we show that the Qian–Wang estimator can be generalized into a broad class of heteroskedasticity-consistent
covariance matrix estimators, and our results can be easily extended to such a class of estimators. |
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Keywords: | |
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