On Standard-Error-Decreasing Complementarity: Why Collinearity is Not the Whole Story

MAGKS No. 03-2017

18 Pages Posted: 25 Feb 2017

See all articles by Bernd Hayo

Bernd Hayo

University of Marburg - School of Business & Economics

Date Written: January 9, 2017

Abstract

There is a widespread belief among economists that adding additional variables to a regression model causes higher standard errors. This note shows that, in general, this belief is unfounded and that the impact of adding variables on coefficients’ standard errors is unclear. The concept of standard-error-decreasing complementarity is introduced, which works against the collinearity-induced increase in standard errors. How standard-error-decreasing complementarity works is illustrated with the help of a nontechnical heuristic, and, using an example based on artificial data, it is shown that the outcome of popular econometric approaches can be potentially misleading.

Keywords: Standard-error-decreasing complementarity, multivariate regression model, standard error, econometric methodology, multicollinearity, collinearity

JEL Classification: C1, B4

Suggested Citation

Hayo, Bernd, On Standard-Error-Decreasing Complementarity: Why Collinearity is Not the Whole Story (January 9, 2017). MAGKS No. 03-2017. Available at SSRN: https://ssrn.com/abstract=2922985 or http://dx.doi.org/10.2139/ssrn.2922985

Bernd Hayo (Contact Author)

University of Marburg - School of Business & Economics ( email )

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Marburg, D-35032
Germany
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