A New Bias Corrected Version of Heteroscedasticity Consistent Covariance Estimator

Munir Ahmed, Muhammad Aslam

Abstract


In the presence of heteroscedasticity, different available flavours of the heteroscedasticity consistent covariance estimator (HCCME) are used. However, the available literature shows that these estimators can be considerably biased in small samples. Cribari–Neto et al. (2000) introduce a bias adjustment mechanism and give the modified White estimator that becomes almost bias-free even in small samples. Extending these results, Cribari-Neto and Galvão (2003) present a similar bias adjustment mechanism that can be applied to a wide class of HCCMEs’. In the present article, we follow the same mechanism as proposed by Cribari-Neto and Galvão to give bias-correction version of HCCME but we use adaptive HCCME rather than the conventional HCCME. The Monte Carlo study is used to evaluate the performance of our proposed estimators.

Keywords


Adaptive estimator; HCCME; Leverage point; Size of test

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DOI: http://dx.doi.org/10.18187/pjsor.v12i2.768

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Title

A New Bias Corrected Version of Heteroscedasticity Consistent Covariance Estimator

Keywords

Adaptive estimator; HCCME; Leverage point; Size of test

Description

In the presence of heteroscedasticity, different available flavours of the heteroscedasticity consistent covariance estimator (HCCME) are used. However, the available literature shows that these estimators can be considerably biased in small samples. Cribari–Neto et al. (2000) introduce a bias adjustment mechanism and give the modified White estimator that becomes almost bias-free even in small samples. Extending these results, Cribari-Neto and Galvão (2003) present a similar bias adjustment mechanism that can be applied to a wide class of HCCMEs’. In the present article, we follow the same mechanism as proposed by Cribari-Neto and Galvão to give bias-correction version of HCCME but we use adaptive HCCME rather than the conventional HCCME. The Monte Carlo study is used to evaluate the performance of our proposed estimators.

Date

2016-06-03

Identifier


Source

Pakistan Journal of Statistics and Operation Research; Vol. 12 No. 2, 2016



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