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

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  • Chien-Chia Liäm Huang
  • Yow-Jen Jou
  • Hsun-Jung Cho

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.

Suggested Citation

  • Chien-Chia Liäm Huang & Yow-Jen Jou & Hsun-Jung Cho, 2017. "VIF-based adaptive matrix perturbation method for heteroskedasticity-robust covariance estimators in the presence of multicollinearity," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 46(7), pages 3255-3263, April.
    • Handle: RePEc:taf:lstaxx:v:46:y:2017:i:7:p:3255-3263
    DOI: 10.1080/03610926.2015.1060340
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    Cited by:

    1. Jaegeum Ryu & Jungha Kim & Smi Choi-Kwon, 2021. "Infection Prevention Performance among In-Flight Cabin Crew in South Korea," IJERPH, MDPI, vol. 18(12), pages 1-11, June.

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