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A Comparison of the Stein-Rule and Positive-Part Stein-Rule Estimators in a Misspecified Linear Regression Model

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Author Info
Ohtani, Kazuhiro

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Abstract

In this paper, we examine the performance of the predictive risk of the Steinrule (SR) and positive-part Stein-rule (PSR) estimators when relevant regressors are omitted in the specified model. The exact formula of the predictive risk of the PSR estimator is derived, and the sufficient condition for the PSR estimator to dominate the SR estimator under a specification error is given. It is shown by numerical computation that the PSR estimator seems to be the best choice among the OLS, SR, and PSR estimators even when there are omitted variables.

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Publisher Info
Article provided by Cambridge University Press in its journal Econometric Theory.

Volume (Year): 9 (1993)
Issue (Month): 04 (August)
Pages: 668-679
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Handle: RePEc:cup:etheor:v:9:y:1993:i:04:p:668-679_00

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  1. Kazuhiro Ohtani, 1998. "An MSE comparison of the restricted Stein-rule and minimum mean squared error estimators in regression," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer, vol. 7(2), pages 361-376, December. [Downloadable!] (restricted)
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