A Robust Heteroskedasticity Consistent Covariance Matrix Estimator

Abstract

To deal with heteroskedasticity of unknown form, this paper suggests to robustly estimate the regression coefficients and then to implement an heteroskedasticity consistent covariance matrix estimator. The robust regression reduces the sample bias of the heteroskedasticity consistent covariance matrix estimator, and does not require the specification of a functional form for heteroskedasticity. Indeed, it replaces each variance with the squared errors, which are in turn estimated by the squared residuals from the robust regression. A Monte-Carlo study verifies the behavior of the proposed estimator, using real data.