More Efficient Tests Robust to Heteroskedasticity of Unknown Form

Emmanuel Flachaire

Econometric Reviews, 2005, vol. 24, issue 2, 219-241

Abstract: In the presence of heteroskedasticity of unknown form, the Ordinary Least Squares parameter estimator becomes inefficient, and its covariance matrix estimator inconsistent. Eicker (1963) and White (1980) were the first to propose a robust consistent covariance matrix estimator, that permits asymptotically correct inference. This estimator is widely used in practice. Cragg (1983) proposed a more efficient estimator, but concluded that tests basd on it are unreliable. Thus, this last estimator has not been used in practice. This article is concerned with finite sample properties of tests robust to heteroskedasticity of unknown form. Our results suggest that reliable and more efficient tests can be obtained with the Cragg estimators in small samples.

Keywords: Heteroskedasticity-robust test; Regression model; Wild bootstrap (search for similar items in EconPapers)
Date: 2005
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Working Paper: More efficient tests robust to heteroskedasticity of unknown form (2005)
Working Paper: More efficient tests robust to heteroskedasticity of unknown form (2005)
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DOI: 10.1081/ETC-200067942

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