Abstract
Panel data estimators can strongly be biased and inconsistent in the presence of heteroscedasticity and anomalous observations called influential observations (IOs) in Random effect (RE) panel data model. The existing methods (LWS, WLSF, WLSDRGP) address only the problem of IO but fail to remedy the combine problem of heteroscedasticity and IOs. Therefore, in this research we develop a method that will remedy the combine problem of heteroscedasticity and IOs based on robust heteroscedasticity consistent covariance matrix (RHCCM) estimator and fast improvised influential distance (FIID) weighting method denoted by WLSFIID. The simulation and numerical evidences show that our proposed estimation method is more efficient than the existing methods by providing smallest bias, and smallest standard error of HC4 and HC5.
Highlights
Panel data is a data that has two dimensionalities
Tables 1-3 shows the performance of the proposed method (WLSFIID) and the existing methods (LWS, WLSF and WLSDRGP), in a simulated heteroscedastic random effect panel data with different sample sizes and influential observations (IOs) contamination level
The results show that the new proposed method WLSFIID is more efficient than the existing methods, by providing less standard error of the estimates, less variances of HC4 and HC5, and produce the coefficient of estimates that is closed to the true parameter coefficient
Summary
Panel data is a data that has two dimensionalities (time series and cross-section dimensions). Researchers developed robust estimators in panel data regression models in order to provide more consistent and efficient estimator (Muhammad et al, 2019; Maronna et al, 2006; Bramati and Croux, 2007; Baltagi, 2008; Baltagi et al, 2009; Verardi and Wagner, 2011; Mazlina and Habshah, 2015, Habshah and Sani, 2018). Their techniques do not take into consideration the combined problem of heteroscedasticity and IO
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