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PMSE performance of the Stein-rule and positive-part Stein-rule estimators in a regression model with or without proxy variables

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  • Namba, Akio
  • Ohtani, Kazuhiro

Abstract

Consider a linear regression model with some relevant regressors are unobservable. In such a situation, we estimate the model by using the proxy variables as regressors or by simply omitting the relevant regressors. In this paper, we derive the explicit formula of the predictive mean squared error (PMSE) of the Stein-rule (SR) estimator and the positive-part Stein-rule (PSR) estimator for the regression coefficients when the proxy variables are used. We examine the effect of using the proxy variables on the risk performances of the SR and PSR estimators. It is shown analytically that the PSR estimator dominates the SR estimator even when the proxy variables are used. Also, our numerical results show that using the proxy variables is preferable to omitting the relevant regressors.

Suggested Citation

  • Namba, Akio & Ohtani, Kazuhiro, 2006. "PMSE performance of the Stein-rule and positive-part Stein-rule estimators in a regression model with or without proxy variables," Statistics & Probability Letters, Elsevier, vol. 76(9), pages 898-906, May.
    • Handle: RePEc:eee:stapro:v:76:y:2006:i:9:p:898-906
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    References listed on IDEAS

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    1. Namba, Akio, 2002. "Pmse Performance Of The Biased Estimators In A Linear Regression Model When Relevant Regressors Are Omitted," Econometric Theory, Cambridge University Press, vol. 18(5), pages 1086-1098, October.
    2. Wickens, Michael R, 1972. "A Note on the Use of Proxy Variables," Econometrica, Econometric Society, vol. 40(4), pages 759-761, July.
    3. Namba, Akio, 2003. "PMSE dominance of the positive-part shrinkage estimator in a regression model when relevant regressors are omitted," Statistics & Probability Letters, Elsevier, vol. 63(4), pages 375-385, July.
    4. Ohtani, Kazuhiro, 1993. "A Comparison of the Stein-Rule and Positive-Part Stein-Rule Estimators in a Misspecified Linear Regression Model," Econometric Theory, Cambridge University Press, vol. 9(4), pages 668-679, August.
    5. Frost, Peter A, 1979. "Proxy Variables and Specification Bias," The Review of Economics and Statistics, MIT Press, vol. 61(2), pages 323-325, May.
    6. Ohtani, Kazuhiro & Hasegawa, Hikaru, 1993. "On Small Sample Properties of R2 in a Linear Regression Model with Multivariate t Errors and Proxy Variables," Econometric Theory, Cambridge University Press, vol. 9(3), pages 504-515, June.
    7. Mittelhammer, R.C., 1984. "Restricted least squares, pre-test, ols and stein rule estimators: Risk comparisons under model misspecification," Journal of Econometrics, Elsevier, vol. 25(1-2), pages 151-164.
    8. McCallum, B T, 1972. "Relative Asymptotic Bias from Errors of Omission and Measurement," Econometrica, Econometric Society, vol. 40(4), pages 757-758, July.
    9. Ohtani, Kazuhiro, 1981. "On the Use of a Proxy Variable in Prediction: An MSE Comparison," The Review of Economics and Statistics, MIT Press, vol. 63(4), pages 627-628, November.
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    Cited by:

    1. Zhang, Xinyu & Chen, Ti & Wan, Alan T.K. & Zou, Guohua, 2009. "Robustness of Stein-type estimators under a non-scalar error covariance structure," Journal of Multivariate Analysis, Elsevier, vol. 100(10), pages 2376-2388, November.

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