文章基本信息

标题：Heteroskedasticity-Consistent Covariance Matrix Estimators in Small Samples with High Leverage Points
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作者：Esra Şimşek ; Mehmet Orhan
期刊名称：Theoretical Economics Letters
印刷版ISSN：2162-2078
电子版ISSN：2162-2086
出版年度：2016
卷号：06
期号：04
页码：658-677
DOI：10.4236/tel.2016.64071
语种：English
出版社：Scientific Research Publishing
摘要：The aim of this paper is to demonstrate the impact of high leverage observations on the performances of prominent and popular Heteroskedasticity-Consistent Covariance Matrix Estimators (HCCMEs) with the help of computer simulation. Firstly, we figure out high leverage observations, then remove them and recalculate the HCCMEs without these observations in order to compare the HCCME performances with and without high leverage points. We identify high leverage observations with the Minimum Covariance Determinant (MCD). We select from among different covariates and disturbance term variances from the related literature in simulation runs in order to compare the percentage difference between the expected value of the HCCME and true covariance matrix as well as the symmetric loss function. Our results revealed that the elimination of high leverage (high MCD distance) observations had improved the HCCME performances considerably and under some settings substantially, depending on the degree of leverage. We hope our theoretical findings will be benefited for practical purposes in applications.
关键词：Heteroskedasticity-Consistent Covariance Matrix Estimator;HCCME;Robust Estimation;MCD;Monte Carlo;Simulation;Loss Functions