VIF-based adaptive matrix perturbation method for heteroskedasticity-robust covariance estimators in the presence of multicollinearity. Issue 7 (3rd April 2017)

• Record Type: Journal Article
• Authors: Huang, Chien-Chia Liäm; Jou, Yow-Jen; Cho, Hsun-Jung
• Is Part Of: Communications in statistics. Volume 46:Issue 7(2017)
• Page Start: 3255
• Page End: 3263
• Publication Date: 2017-04-03
• Subjects: Collinearity -- linear regression -- matrix theory -- optimization; 62J05 -- 62J20; Mathematical statistics -- Periodicals; Mathematics; Statistics; 519.2
• DOI: 10.1080/03610926.2015.1060340
• Languages: English
• ISSNs: 0361-0926

Abstract

ABSTRACT: In this study, we investigate linear regression having both heteroskedasticity and collinearity problems. We discuss the properties related to the perturbation method. Important observations are summarized as theorems. We then prove the main result that states the heteroskedasticity-robust variances can be improved and that the resulting bias is minimized by using the matrix perturbation method. We analyze a practical example for validation of the method.