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Shrinkage estimation in system regression model

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  • Mohammad Arashi
  • Mahdi Roozbeh

Abstract

This article considers the problem of point/set estimation in a specific seemingly unrelated regression model, namely system regression model. Feasible type of shrinkage estimator and its positive part are defined for the effective regression coefficient vector, when the covariance matrix of the error term is assumed to be unknown. Their asymptotic distributional properties are evaluated. Further, related improved confidence set problems are discussed. A simulation study is conducted to evaluate the performance of the estimators. Copyright Springer-Verlag Berlin Heidelberg 2015

Suggested Citation

  • Mohammad Arashi & Mahdi Roozbeh, 2015. "Shrinkage estimation in system regression model," Computational Statistics, Springer, vol. 30(2), pages 359-376, June.
    • Handle: RePEc:spr:compst:v:30:y:2015:i:2:p:359-376
    DOI: 10.1007/s00180-014-0539-5
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    References listed on IDEAS

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    1. Akio Namba & Kazuhiro Ohtani, 2007. "Risk comparison of the Stein-rule estimator in a linear regression model with omitted relevant regressors and multivariatet errors under the Pitman nearness criterion," Statistical Papers, Springer, vol. 48(1), pages 151-162, January.
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    Cited by:

    1. Bahadır Yüzbaşı & S. Ejaz Ahmed, 2020. "Ridge Type Shrinkage Estimation of Seemingly Unrelated Regressions And Analytics of Economic and Financial Data from “Fragile Five” Countries," JRFM, MDPI, vol. 13(6), pages 1-19, June.
    2. J. Kleyn & M. Arashi & S. Millard, 2018. "Preliminary test estimation in system regression models in view of asymmetry," Computational Statistics, Springer, vol. 33(4), pages 1897-1921, December.

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