Bias-corrected heteroscedasticity robust covariance matrix (sandwich) estimators

Two simple bias-corrected sandwich estimators are proposed for the covariance of the least squared coefficient estimator in the linear models. These estimators are unbiased with homoscedastic errors and are shown to be robust against moderate deviations from the homoscedasticity assumption. Simulation results suggest that one of the proposed estimators produces at most a small bias but with an increased variance while the other produces a smaller mean squared error than the classical estimators such as those of Hinkley (1977), White (1980), and Furno (1997) in both cases of homoscedastic and heteroscedastic errors.