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Principal Components and the Problem of Multicollinearity

Published online by Cambridge University Press:  10 May 2017

Bernard J. Morzuch
Bernard J. Morzuch*
Affiliation:
Food and Resource Economics, University of Massachusetts Amherst
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Journal of the Northeastern Agricultural Economics Council , Volume 9 , Issue 1 , April 1980 , pp. 81 - 83
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Copyright © Northeastern Agricultural and Resource Economics Association 

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References

Dixon, W. J. ed. Biomedical Computer Programs (BMDP). Berkeley: University of California Press, 1975.Google Scholar
Fisher, Franklin M. “Tests of Equality Between Sets of Coefficients in Two Linear Regressions: An Expository Note.” Econometrica. 38(1970): 361–6.Google Scholar
Goldberger, Arthur S. Econometric Theory. New York: John Wiley & Sons, Inc. 1964.Google Scholar
Johnson, S. R., Reimer, Steven C., and Rothrock, Thomas P. “Principal Components and the Problem of Multicollinearity.” Metroeconomica. 25(1973): 306–17.Google Scholar
Judge, G. G. and Takayama, T. “Inequality Restrictions in Regression Analysis.” J. Amer. Statist. Assoc. 61(1966): 166–81.Google Scholar
Massy, William. “Principal Components Regression in Exploratory Statistical Research.” J. Amer. Statist. Assoc. 60(1965): 234–56.Google Scholar
Mittelhammer, Ron C. and Baritelle, John L. “On Two Strategies for Choosing Principal Components in Regression Analysis.” Amer. J. Agr. Econ. 59(1977): 336–43.Google Scholar
Morrison, Donald F. Multivariate Statistical Methods. New York: McGraw-Hill Book Co. 1967.Google Scholar
Theil, H. and Goldberger, A. S. “On Pure and Mixed Statistical Estimation in Economics.” Intern. Econ. Rev. 2(1961): 65–78.Google Scholar
Toro-Vizcarrondo, Carlos, and Wallace, T. D. “A Test of the Mean Square Error Criterion for Restrictions in Linear Regression.” J. Amer. Statist. Assoc. 63(1968): 558–72.Google Scholar
Wallace, T. D. “Pretest Estimation in Regression: A Survey.” Amer. J. Agr. Econ. 59(1977): 431–43.Google Scholar
Wallace, T. D. and Toro-Vizcarrondo, C. E. “Tables for the Mean Square Error Test for Exact Linear Restrictions in Regression.” 7. Amer. Statist. Assoc. 64 (1969): 1644–93.Google Scholar
White, Kenneth J. SHAZAM (Computer Manual). Version 2.3. Department of Economics, Rice University. August 1978.Google Scholar
Willis, Cleve E. and Perlack, Robert D., et al. “Multicollinearity: Effects, Symptoms, and Remedies.” Journal of the Northeastern Agricultural Economics Council. 7 (1978): 55–61.Google Scholar