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Multicollinearity In Applied Economics Research And The Bayesian Linear Regression

Author

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  • Eisenstat, Eric

    (Department of Economics, University of California, Irvine, United States)

Abstract

This article revises the popular issue of collinearity amongst explanatory variables in the context of a multiple linear regression analysis, particularly in empirical studies within social science related fields. Some important interpretations and explanations are highlighted from the econometrics literature with respect to the effects of multicollinearity on statistical inference, as well as the general shortcomings of the once fervent search for methods intended to detect and mitigate these effects. Consequently, it is argued and demonstrated through simulation how these views may be resolved against an alternative methodology by integrating a researcher’s subjective information in a formal and systematic way through a Bayesian approach.

Suggested Citation

  • Eisenstat, Eric, 2009. "Multicollinearity In Applied Economics Research And The Bayesian Linear Regression," Annals of Spiru Haret University, Economic Series, Universitatea Spiru Haret, vol. 1(1), pages 47-60.
    • Handle: RePEc:ris:sphecs:0004
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    More about this item

    Keywords

    multiple linear regressions; classical normal regression; collinearity; multicollinearity; classical inference; subjective probability; Bayesian linear regression; prior information; posterior distributions; simulation;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General

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