Munich Personal RePEc Archive

A Minimax Bias Estimator for OLS Variances under Heteroskedasticity

Ahmed, Mumtaz and Zaman, Asad (2014): A Minimax Bias Estimator for OLS Variances under Heteroskedasticity.


Download (903kB) | Preview


Analytic evaluation of heteroskedasticity consistent covariance matrix estimates (HCCME) is difficult because of the complexity of the formulae currently available. We obtain new analytic formulae for the bias of a class of estimators of the covariance matrix of OLS in a standard linear regression model. These formulae provide substantial insight into the properties and performance characteristics of these estimators. In particular, we find a new estimator which minimizes the maximum possible bias and improves substantially on the standard Eicker-White estimate.

MPRA is a RePEc service hosted by
the Munich University Library in Germany.