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On Two Strategies for Choosing Principal Components in Regression Analysis

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  • Ron C. Mittelhammer
  • John L. Baritelle

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.

Suggested Citation

  • Ron C. Mittelhammer & John L. Baritelle, 1977. "On Two Strategies for Choosing Principal Components in Regression Analysis," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 59(2), pages 336-343.
    • Handle: RePEc:oup:ajagec:v:59:y:1977:i:2:p:336-343.
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    File URL: http://hdl.handle.net/10.2307/1240024
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    Citations

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    Cited by:

    1. Jones, Eugene, 1985. "The Use of Principal Components in Simultaneous Equations: An Empirical Application," 1985 Annual Meeting, August 4-7, Ames, Iowa 278516, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    2. Morzuch, Bernard J., 1980. "Principal Components And The Problem Of Multicollinearity," Northeastern Journal of Agricultural and Resource Economics, Northeastern Agricultural and Resource Economics Association, vol. 0(Number 1), pages 1-3, April.
    3. Willis, Cleve E. & Perlack, Robert D., 1978. "Multicollinearity: Effects, Symptoms, And Remedies," Northeastern Journal of Agricultural and Resource Economics, Northeastern Agricultural and Resource Economics Association, vol. 0(Number 1), pages 1-7, April.
    4. Willis, Cleve E. & Perlack, Robert D., 1978. "Multicollinearity: Effects, Symptoms, And Remedies," Journal of the Northeastern Agricultural Economics Council, Northeastern Agricultural and Resource Economics Association, vol. 7(1), pages 1-7, April.
    5. Marconi, Gabriele, 2014. "European higher education policies and the problem of estimating a complex model with a small cross-section," MPRA Paper 87600, University Library of Munich, Germany.
    6. Zhang, Cheng & Wang, Qunwei & Shi, Dan & Li, Pengfei & Cai, Wanhuan, 2016. "Scenario-based potential effects of carbon trading in China: An integrated approach," Applied Energy, Elsevier, vol. 182(C), pages 177-190.
    7. Morzuch, Bernard J., 1980. "Principal Components And The Problem Of Multicollinearity," Journal of the Northeastern Agricultural Economics Council, Northeastern Agricultural and Resource Economics Association, vol. 9(1), pages 1-3, April.

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