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Double Shrinkage Estimation of Common Coefficients in Two Regression Equations with Heteroscedasticity

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  • Kubokawa, Tatsuya
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    Abstract

    The problem of estimating the common regression coefficients is addressed in this paper for two regression equations with possibly different error variances. The feasible generalized least squares (FGLS) estimators have been believed to be admissible within the class of unbiased estimators. It is, nevertheless, established that the FGLS estimators are inadmissible in light of minimizing the covariance matrices if the dimension of the common regression coefficients is greater than or equal to three. Double shrinkage unbiased estimators are proposed as possible candidates of improved procedures.

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    File URL: http://www.sciencedirect.com/science/article/B6WK9-45KNB4H-3/2/4ed527d94fbe5ddd7100dd9ef108ab18
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    Bibliographic Info

    Article provided by Elsevier in its journal Journal of Multivariate Analysis.

    Volume (Year): 67 (1998)
    Issue (Month): 2 (November)
    Pages: 169-189

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    Handle: RePEc:eee:jmvana:v:67:y:1998:i:2:p:169-189

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    Related research

    Keywords: common mean problem feasible (two-stage) generalized least squares estimators inadmissibility unbiased estimation heteroscedastic linear regression model;

    References

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    1. Swamy, P. A. V. B. & Mehta, J. S., 1979. "Estimation of common coefficients in two regression equations," Journal of Econometrics, Elsevier, vol. 10(1), pages 1-14, April.
    2. Taylor, William E, 1977. "Small Sample Properties of a Class of Two Stage Aitken Estimators," Econometrica, Econometric Society, vol. 45(2), pages 497-508, March.
    3. Taylor, William E, 1978. "The Heteroscedastic Linear Model: Exact Finite Sample Results," Econometrica, Econometric Society, vol. 46(3), pages 663-75, May.
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    Cited by:
    1. Yang, Guo-Qing & Wu, Qi-Guang, 2004. "Existence conditions for the uniformly minimum risk unbiased estimators in a class of linear models," Journal of Multivariate Analysis, Elsevier, vol. 88(1), pages 76-88, January.

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