VIF-based adaptive matrix perturbation method for heteroskedasticity-robust covariance estimators in the presence of multicollinearity

Chien Chia Liäm Huang*, Yow Jen Jou, Hsun-Jung Cho

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

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.

Original languageEnglish
Pages (from-to)3255-3263
Number of pages9
JournalCommunications in Statistics - Theory and Methods
Volume46
Issue number7
DOIs
StatePublished - 3 Apr 2017

Keywords

  • Collinearity
  • linear regression
  • matrix theory
  • optimization

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