Robust multicollinearity diagnostic measure for fixed effect panel data model
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 ord...
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| Format: | Article |
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Universiti Teknologi Malaysia
2021
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| Online Access: | http://psasir.upm.edu.my/id/eprint/94986/ |
| _version_ | 1848862045729980416 |
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| author | Ismaeel, Shelan Saied Midi, Habshah Sani, Muhammad |
| author_facet | Ismaeel, Shelan Saied Midi, Habshah Sani, Muhammad |
| author_sort | Ismaeel, Shelan Saied |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | 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 (WGM-DRGP) 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. |
| first_indexed | 2025-11-15T13:10:47Z |
| format | Article |
| id | upm-94986 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-15T13:10:47Z |
| publishDate | 2021 |
| publisher | Universiti Teknologi Malaysia |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-949862023-01-09T02:13:28Z http://psasir.upm.edu.my/id/eprint/94986/ Robust multicollinearity diagnostic measure for fixed effect panel data model Ismaeel, Shelan Saied Midi, Habshah Sani, Muhammad 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 (WGM-DRGP) 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. Universiti Teknologi Malaysia 2021-10-30 Article PeerReviewed Ismaeel, Shelan Saied and Midi, Habshah and Sani, Muhammad (2021) Robust multicollinearity diagnostic measure for fixed effect panel data model. Malaysian Journal of Fundamental and Applied Sciences, 17 (5). 636 - 646. ISSN 2289-5981; ESSN: 2289-599X https://mjfas.utm.my/index.php/mjfas/article/view/2391 10.11113/mjfas.v17n5.2391 |
| spellingShingle | Ismaeel, Shelan Saied Midi, Habshah Sani, Muhammad Robust multicollinearity diagnostic measure for fixed effect panel data model |
| title | Robust multicollinearity diagnostic measure for fixed effect panel data model |
| title_full | Robust multicollinearity diagnostic measure for fixed effect panel data model |
| title_fullStr | Robust multicollinearity diagnostic measure for fixed effect panel data model |
| title_full_unstemmed | Robust multicollinearity diagnostic measure for fixed effect panel data model |
| title_short | Robust multicollinearity diagnostic measure for fixed effect panel data model |
| title_sort | robust multicollinearity diagnostic measure for fixed effect panel data model |
| url | http://psasir.upm.edu.my/id/eprint/94986/ http://psasir.upm.edu.my/id/eprint/94986/ http://psasir.upm.edu.my/id/eprint/94986/ |