More efficient tests robust to heteroskedasticity of unknown form

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 thattests based on it are unreliable. Thus, this last estimator has not been used in practice. This paper is concerned with finite sample properties of tests robust to heteroskedasticity of unknown form. Our results suggestthat reliable and more efficient tests can be obtained with the Cragg estimators in small samples.wild bootstrap; heteroskedasticity-robust test; regression model