Inference Under Heteroskedasticity and Leveraged Data
- 16 July 2007
- journal article
- conference paper
- Published by Taylor & Francis in Communications in Statistics - Theory and Methods
- Vol. 36 (10) , 1877-1888
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 Cribari-Neto , F. ( 2004 ). Asymptotic inference under heteroskedasticity of unknown form . Computat. Statist. Data Anal. 45 : 215 – 233 . [Crossref], [Web of Science ®] [Google Scholar] ). 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
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