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Multicollinearity: Effects, Symptoms, and Remedies

Published online by Cambridge University Press:  10 May 2017

Cleve E. Willis  and
Robert D. Perlack
Cleve E. Willis
Affiliation:
Food and Resource Economics, University of Massachusetts
Robert D. Perlack
Affiliation:
Food and Resource Economics, University of Massachusetts
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Abstract

Multicollinearity is one of several problems confronting researchers using regression analysis. This paper examines the regression model when the assumption of independence among the independent variables is violated. The basic properties of the least squares approach are examined, the concept of multicollinearity and its consequences on the least squares estimators are explained. The detection of multicollinearity and alternatives for handling the problem are then discussed. The alternative approaches evaluated are variable deletion, restrictions on the parameters, ridge regression and Bayesian estimation.

Type
Articles
Information
Journal of the Northeastern Agricultural Economics Council , Volume 7 , Issue 1 , April 1978 , pp. 55 - 61
Copyright
Copyright © Northeastern Agricultural and Resource Economics Association 

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Footnotes

This paper emanated in part from a course in Econometrics at the University of Massachusetts. The following students are co-authors of this article: David Himelfarb, Thomas McBride, Philip Sczerzenie, Vivien Singer, Franklin Tirsch, Stuart Westin, and Yisehac Yohannes. This paper was made possible by support from the University of Massachusetts Experiment Station.

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