On Inference of the Linear Regression Model with Groupwise Heteroscedasticity

Abdul Majid, Muhammad Aslam, Muhammad Amanullah, Saima Altaf

Abstract


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.

Keywords


Groupwise heteroscedasticity, HCCME, Size distortion, White’s estimator

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DOI: http://dx.doi.org/10.18187/pjsor.v13i2.1345

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Title

On Inference of the Linear Regression Model with Groupwise Heteroscedasticity

Keywords

Groupwise heteroscedasticity, HCCME, Size distortion, White’s estimator

Description

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.

Date

2017-06-01

Identifier


Source

Pakistan Journal of Statistics and Operation Research; Vol. 13 No. 2, 2017



Print ISSN: 1816-2711 | Electronic ISSN: 2220-5810