期刊名称：Pakistan Journal of Statistics and Operation Research
印刷版ISSN：2220-5810
出版年度：2017
卷号：13
期号：2
页码：265-276
DOI：10.18187/pjsor.v13i2.1345
语种：English
出版社：College of Statistical and Actuarial Sciences
摘要：The performance of heteroscedasticity consistent covariance matrix estimators (HCCMEs), namely, HC0, HC1, HC2, HC3 and HC4 have been evaluated by numerous researchers for the heteroscedastic linear regression models. This study focuses on examining the performance of these covariance estimators in case of groupwise heteroscedasticity. With the help of the Monte Carlo simulations, we evaluate the performance of these covariance estimators and the associated quasi-t tests. We consider the cases when data are divided into 10, 20 and 30 groups of different sizes and the regression is run on the mean values of the dependent variable and the regressor of these groups. The numerical results show that HCCMEs perform appealingly well in case of groupwise heteroscedasticity.
其他摘要：The performance of heteroscedasticity consistent covariance matrix estimators (HCCMEs), namely, HC0, HC1, HC2, HC3 and HC4 have been evaluated by numerous researchers for the heteroscedastic linear regression models. This study focuses on examining the performance of these covariance estimators in case of groupwise heteroscedasticity. With the help of the Monte Carlo simulations, we evaluate the performance of these covariance estimators and the associated quasi-t tests. We consider the cases when data are divided into 10, 20 and 30 groups of different sizes and the regression is run on the mean values of the dependent variable and the regressor of these groups. The numerical results show that HCCMEs perform appealingly well in case of groupwise heteroscedasticity.
