Author(s):
1. Munir Ahamed:
Department of Management Sciences COMSATS Institute of Information Technology, Vehari, Pakistan.
2. Muhammad Aslam:
Department of Statistics, Bahauddin Zakariya University, Multan, Pakistan.
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.
Page(s):
389-405
DOI:
DOI not available
Published:
Journal: Pakistan Journal of Statistics and Operation Research, Volume: 12, Issue: 2, Year: 2016
Keywords:
Adaptive estimator
,
HCCME
,
Size of test
,
Leverage point
References:
References are not available for this document.
Citations
Citations are not available for this document.