The performance of robust heteroscedasticity consistent covariance matrix estimator

The weighted least squares (WLS) method together with heteroscedasticity consistent covariance matrix (HCCM) estimator is often used to estimate the parameters of a heteroscedastic regression model when the form of errors structure is unknown. However, WLS based on weight determined by hat matrix suffers much set back in the presence of high leverage points (HLPs) in a data set. Moreover, the use of WLS requires an efficient weighting method that will successfully down weight HLPs. In this paper, we proposed new weighting method based on HLPs detection measure in which the good leverage points are allowed to contribute in the estimation of parameters and the bad leverage points are down weighted as they are responsible for the deviation of the model fit. In the proposed method we employed modified generalized studentized residuals (MGt) with diagnostic robust generalized potentials based on index set equality (DRGPISE) termed FMGt on HCCM estimator. The performance of the proposed weighting method is assessed by generated artificial data set.

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