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Collinearity diagnostic applied in ridge estimation through the variance inflation factor

Author

Listed:
  • Roman Salmerón Gómez
  • José García Pérez
  • María Del Mar López Martín
  • Catalina García García

Abstract

The variance inflation factor (VIF) is used to detect the presence of linear relationships between two or more independent variables (i.e. collinearity) in the multiple linear regression model. However, the traditionally used VIF definitions encounter some problems when extended to the case of the ridge estimation (RE). This paper presents an extension of the VIF in RE by providing two alternative VIF expressions that overcome these problems in the general case. Some characteristics of these expressions are also presented and compared with the traditional expression. The results are illustrated with an economic example in the case of three independent variables and with a Monte Carlo simulation for the general case.

Suggested Citation

  • Roman Salmerón Gómez & José García Pérez & María Del Mar López Martín & Catalina García García, 2016. "Collinearity diagnostic applied in ridge estimation through the variance inflation factor," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(10), pages 1831-1849, August.
    • Handle: RePEc:taf:japsta:v:43:y:2016:i:10:p:1831-1849
    DOI: 10.1080/02664763.2015.1120712
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    References listed on IDEAS

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    1. Wichers, C Robert, 1975. "The Detection of Multicollinearity: A Comment," The Review of Economics and Statistics, MIT Press, vol. 57(3), pages 366-368, August.
    2. José Dias Curto & José Castro Pinto, 2007. "New Multicollinearity Indicators in Linear Regression Models," International Statistical Review, International Statistical Institute, vol. 75(1), pages 114-121, April.
    3. Belsley, David A., 1982. "Assessing the presence of harmful collinearity and other forms of weak data through a test for signal-to-noise," Journal of Econometrics, Elsevier, vol. 20(2), pages 211-253, November.
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