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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.


Adaptive estimator HCCME Leverage point Size of test

Article Details

Author Biography

Munir Ahmed, COMSATS Institute of Information Technology, Vehari Campus

Munir Ahmed

Assistant Professor

Department of Management Sciences

COMSATS Institute of Information Technology, Vehari Campus

How to Cite
Ahmed, M., & Aslam, M. (2016). A New Bias Corrected Version of Heteroscedasticity Consistent Covariance Estimator. Pakistan Journal of Statistics and Operation Research, 12(2), 389-405.