2021-08

online

It is now evident that high leverage points (HLPs) can induce the multicollinearity pattern of a data in fixed effect panel data model. Those observations that are responsible for this phenomenon are called high leverage collinearity-enhancing observations (HLCEO). The commonly used within group ordinary least squares (WOLS) estimator for estimating the parameters of fixed effect panel data model is easily affected by HLCEOs. In their presence, the WOLS estimates may produce large variances and this would lead to erroneous interpretation. Therefore, it is imperative to detect the multicollinearity which is caused by HLCEOs. The classical Variance Inflation Factor (CVIF) is the commonly used diagnostic method for detecting multicollinearity in panel data. However, it is not correctly diagnosed multicollinearity in the presence of HLCEOs. Hence, in this paper three new robust diagnostic methods of diagnosing multicollinearity in panel data are proposed, namely the RVIF (WGM-FIMGT), RVIF (WGMDRGP) and RVIF (WMM) and compared their performances with the CVIF. The numerical evidences show that the CVIF incorrectly diagnosed multicollinearity but our proposed methods correctly diagnosed no multicollinearity in the presence of HLCEOs where RVIF (WGM-FIMGT) being the best method as it has the least computational running time.

2017-05

Langkawi, Malaysia.

The detection of multicollinearity is very crucial so that proper remedial measures can be taken up in their presence. The widely used diagnostic method to detect multicollinearity in multiple linear regression is by using Classical Variance Inflation Factor (CVIF). It is now evident that the CVIF failed to correctly detect multicollinearity when high leverage points are present in a set of data. Robust Variance Inflation Factor (RVIF) has been introduced to remedy this problem. Nonetheless, the computation of RVIF takes longer time because it is based on robust GM (DRGP) estimator which depends on Minimum Volume Ellipsoid (MVE) estimator that involves a lot of computer times.. In this paper, we propose a fast RVIF (FRVIF) which take less computing time. The results of the simulation study and numerical examples indicate that our proposed FRVIF successfully detect multicollinearity problem with faster rate compared to other methods.