Improved estimation under collinearity and squared error loss

Carter Hill and George Judge ()

Journal of Multivariate Analysis, 1990, vol. 32, issue 2, 296-312

Abstract: This paper examines the performance of several biased, Stein-like and empirical Bayes estimators for the general linear statistical model under conditions of collinearity. A new criterion for deleting principal components, based on an unbiased estimator of risk, is introduced. Using a squared error measure and Monte Carlo sampling experiments, the resulting estimator's performance is evaluated and compared with other traditional and non-traditional estimators.

Keywords: multicollinearity; principal; components; linear; regression; Stein; rules; empirical; Bayes; estimators; unbiased; estimation; of; risk (search for similar items in EconPapers)
Date: 1990
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Citations: View citations in EconPapers (2)

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