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On Two Strategies for Choosing Principal Components in Regression Analysis
Author(s) -
Mittelhammer Ron C.,
Baritelle John L.
Publication year - 1977
Publication title -
american journal of agricultural economics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.949
H-Index - 111
eISSN - 1467-8276
pISSN - 0002-9092
DOI - 10.2307/1240024
Subject(s) - multicollinearity , statistics , mean squared error , ordinary least squares , principal component analysis , econometrics , regression , monte carlo method , mathematics , sample size determination , sample (material) , regression analysis , chemistry , chromatography
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 R 2 , 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.