Inference Under Heteroskedasticity and Leveraged Data

Abstract

We evaluate the finite-sample behavior of different heteros-ke-das-ticity-consistent covariance matrix estimators, under both constant and unequal error variances. We consider the estimator proposed by Halbert White (HC0), and also its variants known as HC2, HC3, and HC4; the latter was recently proposed by Cribari-Neto (2004). We propose a new covariance matrix estimator: HC5. It is the first consistent estimator to explicitly take into account the effect that the maximal leverage has on the associated inference. Our numerical results show that quasi-t inference based on HC5 is typically more reliable than inference based on other covariance matrix estimators.

Keywords:

• Covariance matrix estimation
• Heteroskedasticity
• Leverage points
• Linear regression

Mathematics Subject Classification:

• Primary 62J05
• Secondary 62F10, 62F03

View correction statement:

Errata: Inference Under Heteroskedasticity and Leveraged Data, Communications in Statistics, Theory and Methods, 36, 1877–1888, 2007

Acknowledgments

We thank Spyros G. Zarkos for comments and suggestions. We also gratefully acknowledge partial financial support from CAPES and CNPq.