How do HCCMEs perform in small samples?

The purpose of this paper is to explore the performances of prominent and popular heteroscedasticity-consistent covariance matrix estimators (HCCMEs) in small samples. The HCCMEs are selected from the literature and their ability to estimate the true covariance matrix of the coefficient's vector is evaluated through simulation runs. We calculate the percentage difference between the expected value of the HCCME and the true covariance matrix to set a convenient stage to make the comparisons under several different regression settings. The main contribution of the paper is the inclusion of the HCCMEs that have been introduced into the literature recently. We report the performances of the HCCMEs under different settings of the covariates and error term variances. We let the covariates follow uniform, normal, Student's t, and Cauchy distributions and tailor the error term variances to increase gradually. [ABSTRACT FROM AUTHOR]