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Revisiting the statistical specification of near-multicollinearity in the logistic regression model

Author

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  • Atems Bebonchu

    (Economics and Financial Studies, School of Business, Clarkson University, 8 Clarkson Avenue, Potsdam, NY 13699, USA)

  • Bergtold Jason

    (Kansas State University, Agricultural Economics, 342 Waters Hall Ag Economics Kansas State University, Manhattan, KS 66506-4011, USA)

Abstract

This paper revisits the statistical specification of near-multicollinearity in the logistic regression model. We argue that the ceteris paribus clause, which assumes that the maximum likelihood estimator of β remains constant as the correlation (ρ) between the regressors increases, invoked under the traditional account of near-multicollinearity is rather misleading. We derive the parameters of the logistic regression model and show that they are functions of ρ, indicating that the ceteris paribus clause is unattainable. Monte Carlo simulations confirm these findings and further show that: coefficient estimates and related statistics fluctuate in a non-symmetric, non-monotonic way as |ρ|→1; that the impact of near-multicollinearity is centered on the estimates of β; and that the impact on substantive inferences does not necessarily follow what the traditional account implies.

Suggested Citation

  • Atems Bebonchu & Bergtold Jason, 2016. "Revisiting the statistical specification of near-multicollinearity in the logistic regression model," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 20(2), pages 199-210, April.
    • Handle: RePEc:bpj:sndecm:v:20:y:2016:i:2:p:199-210:n:1
    DOI: 10.1515/snde-2013-0052
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    References listed on IDEAS

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    1. Spanos,Aris, 1986. "Statistical Foundations of Econometric Modelling," Cambridge Books, Cambridge University Press, number 9780521269124, June.
    2. Gourieroux,Christian, 2000. "Econometrics of Qualitative Dependent Variables," Cambridge Books, Cambridge University Press, number 9780521331494, June.
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    Cited by:

    1. John Komlos, 2020. "Multicollinearity in the Presence of Errors-in-Variables Can Increase the Probability of Type-I Error," Journal of Economics and Econometrics, Economics and Econometrics Society, vol. 63(1), pages 1-17.

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    More about this item

    Keywords

    logistic regression; model diagnostics; near-multicollinearity;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics

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