Inference Under Heteroskedasticity and Leveraged Data |
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Authors: | Francisco Cribari-Neto Tatiene C Souza Klaus L P Vasconcellos |
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Institution: | 1. Departamento de Estatística , Universidade Federal de Pernambuco , Recife, Brazil cribari@de.ufpe.br;3. Departamento de Estatística , Universidade Federal da Bahia , Salvador, Brazil;4. Departamento de Estatística , Universidade Federal de Pernambuco , Recife, Brazil |
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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. |
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Keywords: | Covariance matrix estimation Heteroskedasticity Leverage points Linear regression |
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