On Two Strategies for Choosing Principal Components in Regression Analysis

Ron Mittelhammer () and John L. Baritelle

American Journal of Agricultural Economics, 1977, vol. 59, issue 2, 336-343

Abstract: Two traditional methods used to form principal components (PC) regression estimates are reviewed, and small sample properties of the estimates are compared with OLS estimates. A Monte Carlo experiment is used to facilitate comparisons. Theoretical considerations and empirical observation indicate that the PC techniques tend to produce estimates lower in mean square error (MSE) than OLS estimates under conditions of high multicollinearity, low R2, and small sample size. Although under these conditions the PC techniques may be preferred to OLS in the relative MSE sense, MSE in the absolute sense may still render the PC estimates useless in applications.

Date: 1977
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Persistent link: https://EconPapers.repec.org/RePEc:oup:ajagec:v:59:y:1977:i:2:p:336-343.

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