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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.

References

Bellman, R., Adaptive Control Processes: A Guided Tour, Santa Monica: RAND Corporation, 1961.Google Scholar
Brown, W. and Beattie, B., “Improving Estimates of Economic Parameters by Use of Ridge Regression with Production Function Applications”, AJAE, 22: 21–32, February 1975.Google Scholar
Chowdhury, S. R., et al., “A Bayesian Application on Cobb-Douglas Production Function,” AJAE, May 1975, pp. 361–363.Google Scholar
Doll, J., “On Exact Multicollinearity and the Estimation of the Cobb-Douglas Production Function”, AJAE, 56: 3: 556–563, August 1974.Google Scholar
Farrar, D. E. and Glauber, R. R., “Multicollinearity in Regression Analysis: The Problem Re-visited”, Review of Economics and Statistics, 49: 92–107, 1967.Google Scholar
Freud, R. D. Oebertin, “Variable Selection and Statistical Significance: A Sampling Experiment”, AJAE, 57: 4: 721–722, November 1975.Google Scholar
Johnston, Jr., Econometric Methods, New York: McGraw-Hill, 1972.Google Scholar
Judge, G. and Takayama, T., “Inequality Restrictions in Regression Analysis”, JASA, 61: 166–181, March 1966.Google Scholar
Kendall, M., A Course in Multivariate Analysis, London: Charles Griffin, 1957.Google Scholar
Klein, L. R., An Introduction to Econometrics, Prentice-Hall, 1962.Google Scholar
Lindsay, Bruce E. and Willis, Cleve E., “Factors Influencing Land Values in the Presence of Suburban Sprawl”, Journal of the Northeastern Agricultural Economics Council, 3: 1: 112–124, May 1974.Google Scholar
McCallum, B., “Artificial Orthogonalization in Regression Analysis”, Review of Economics and Statistics, 52: 110–113, 1970.Google Scholar
Mittlehammer, R. and Baritelle, J., “On Two Strategies for Choosing Principal Components in Regression Analysis,” AJAE, 59: 2: 336–343, May 1977.Google Scholar
Rausser, G; Willis, C.; and Frick, P., “Learning, External Benefits, and Subsidies in Water Desalination,” Water Resources Research, 8: 6: 1385–1400, December 1972.Google Scholar
Tiao, G. and Zellner, A., “Bayes’ Theorem and the Use of Prior Knowledge in Regression Analysis”, Biometrica, 51: 219–230, 1964.Google Scholar
Wallace, T., “Pretest Estimation in Regression: A Survey”, AJAE, August 1977, pp. 431–443.Google Scholar
White, K., “A General Computer Program for Econometric Methods—SHAZAM”, Econometrica, 46: 1: 239–240, January 1978.Google Scholar
Zellner, A., An Introduction to Bayesian Inference in Econometrics, New York: John Wiley and Sons, 1971.Google Scholar