Improving the Stein-Rule Estimator of Each Individual Regression Coefficient Using the Stein Varience Estimator

  • Kazuhiro Ohtani
Part of the Advanced Studies in Theoretical and Applied Econometrics book series (ASTA, volume 35)


A substantial body of literature on regression has focused on estimators which are biased but more precise than the ordinary least squares (OLS) estimator. As is discussed in Paelinck and Klaasseen (1979), estimation of regression parameters is important in spatial econometric models, and the precision of estimation is often measured by the mean square error (MSE). It is well known that when three or more coefficients are estimated simultaneously, the Stein-rule (SR) estimator proposed by Stein (1955) and James and Stein (1961) dominates the OLS estimator in terms of MSE. [Exactly speaking, in terms of predictive MSE.] Further, the positive-part Stein-rule (PSR) estimator dominates the SR estimator in terms of MSE. [See, for example, Judge and Yancey (1986).


Mean Square Error Ordinary Little Square Monte Carlo Experiment Ordinary Little Square Estimator Mean Square Error Performance 
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© Springer Science+Business Media Dordrecht 1998

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  • Kazuhiro Ohtani

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