Mean square error matrix comparison of some estimators in linear regressions with multicollinearity

Nityananda Sarkar

Statistics & Probability Letters, 1996, vol. 30, issue 2, 133-138

Abstract: The ordinary least squares, the principal components regression and the ordinary ridge regression estimators are special cases of the r - k class estimator proposed by Baye and Parker (1984) for regression models with multicollinearity. We obtain necessary and sufficient conditions for the superiority of the r - k class estimator over each of these three estimators by the criterion of mean square error matrix. We also suggest tests to verify if these conditions are indeed satisfied.

Keywords: Mean; square; error; matrix; Multicollinearity; Ordinary; ridge; regression; estimator; Principal; components; regression; estimator; r; -; k; class; estimator (search for similar items in EconPapers)
Date: 1996
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Citations: View citations in EconPapers (8)

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