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Further improving the Stein-rule estimator using the Stein variance estimator in a misspecified linear regression model

Listed author(s):
  • Ohtani, Kazuhiro

In this paper, we consider a linear regression model when relevant regressors are omitted in the specified model. We examine the MSE dominance of the pre-test Stein-rule estimator of regression coefficients using the Stein-variance estimator over the traditional Stein-rule estimator of regression coefficients.

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Article provided by Elsevier in its journal Statistics & Probability Letters.

Volume (Year): 29 (1996)
Issue (Month): 3 (September)
Pages: 191-199

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Handle: RePEc:eee:stapro:v:29:y:1996:i:3:p:191-199
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  1. Ohtani, Kazuhiro, 1988. "Optimal levels of significance of a pre-test in estimating the disturbance variance after the pre-test for a linear hypothesis on coefficients in a linear regression," Economics Letters, Elsevier, vol. 28(2), pages 151-156.
  2. Gelfand, Alan E. & Dey, Dipak K., 1988. "Improved estimation of the disturbance variance in a linear regression model," Journal of Econometrics, Elsevier, vol. 39(3), pages 387-395, November.
  3. Giles, Judith A., 1991. "Pre-testing for linear restrictions in a regression model with spherically symmetric disturbances," Journal of Econometrics, Elsevier, vol. 50(3), pages 377-398, December.
  4. 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.
  5. Kubokawa, Tatsuya, 1991. "An approach to improving the James-Stein estimator," Journal of Multivariate Analysis, Elsevier, vol. 36(1), pages 121-126, January.
  6. 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(04), pages 668-679, August.
  7. Berry, J. Calvin, 1994. "Improving the James-Stein estimator using the Stein variance estimator," Statistics & Probability Letters, Elsevier, vol. 20(3), pages 241-245, June.
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