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Multicollinearity in the Presence of Errors-in-Variables Can Increase the Probability of Type-I Error

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  • John Komlos
Abstract
Multicollinearity, in combination with errors-in-variables, can increase the likelihood of a Type-I error by inflating the value of the estimated coefficients by more than it magnifies their standard errors, thereby increasing the likelihood of obtaining statistically significant results. This anomalous result may be caused by an interaction effect between errors-in-variables and multicollinearity.

Suggested Citation

  • 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.
    • Handle: RePEc:eei:journl:v:63:y:2020:i:1:p:1-17
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    References listed on IDEAS

    as
    1. Chatelain, Jean-Bernard & Ralf, Kirsten, 2014. "Spurious regressions and near-multicollinearity, with an application to aid, policies and growth," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 39(A), pages 85-96.
    2. Bodenhorn, Howard & Guinnane, Timothy W. & Mroz, Thomas A., 2017. "Sample-Selection Biases and the Industrialization Puzzle," The Journal of Economic History, Cambridge University Press, vol. 77(1), pages 171-207, March.
    3. Chatelain, Jean-Bernard, 2010. "Can statistics do without artefacts?," MPRA Paper 42867, University Library of Munich, Germany.
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    5. Carl Mela & Praveen Kopalle, 2002. "The impact of collinearity on regression analysis: the asymmetric effect of negative and positive correlations," Applied Economics, Taylor & Francis Journals, vol. 34(6), pages 667-677.
    6. Brian A'Hearn & John Komlos, 2015. "The Decline in the Nutritional Status of the U.S. Antebellum Population at the Onset of Modern Economic Growth," CESifo Working Paper Series 5691, CESifo.
    7. 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.
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    More about this item

    Keywords

    Multicollinearity; errors-in-variables; Type-I error;
    All these keywords.

    JEL classification:

    • J12 - Labor and Demographic Economics - - Demographic Economics - - - Marriage; Marital Dissolution; Family Structure

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