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MTest: a bootstrap test for multicollinearity

Author

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  • Morales-Oñate, Víctor
  • Morales-Oñate, Bolívar

Abstract

A non parametric test based on bootstrap for detecting multicollinearity is proposed: MTest. This test gives statistical support to two of the most famous methods for detecting multicollinearity in applied work: Klein's rule and Variance Inflation Factor (VIF). Mtest lets the researcher set a statistical significance, or more precisely, an achieved significance level (ASL). In order to show the benefits of MTest, the procedure is computationally implemented in a function for linear regression models. These function is tested in numerical experiments that match the expected results. Finally, this paper makes an application of MTest to real data known to have multicollinearity problems and successfully detects multicollinearity with a given ASL.

Suggested Citation

  • Morales-Oñate, Víctor & Morales-Oñate, Bolívar, 2021. "MTest: a bootstrap test for multicollinearity," MPRA Paper 112332, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:112332
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    File URL: https://mpra.ub.uni-muenchen.de/112332/1/MPRA_paper_112332.pdf
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    References listed on IDEAS

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    1. Leamer, Edward E., 1983. "Model choice and specification analysis," Handbook of Econometrics, in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 1, chapter 5, pages 285-330, Elsevier.
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    More about this item

    Keywords

    MTest; Multicollinearity; Non Parametric Statistics; Simulation;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General

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