Separate versus system methods of Stein-rule estimation in seemingly unrelated regression models

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Original languageEnglish
Pages (from-to)2077-2099
Journal / PublicationCommunications in Statistics - Theory and Methods
Issue number11
Publication statusPublished - Nov 2002


Despite the sizeable literature associated with the seemingly unrelated regression models, not much is known about the use of Stein-rule estimators in these models. This gap is remedied in this paper, in which two families of Stein-rule estimators in seemingly unrelated regression equations are presented and their large sample asymptotic properties explored and evaluated. One family of estimators uses a shrinkage factor obtained solely from the equation under study while the other has a shrinkage factor based on all the equations of the model. Using a quadratic loss measure and Monte-Carlo sampling experiments, the finite sample risk performance of these estimators is also evaluated and compared with the traditional feasible generalized least squares estimator.

Research Area(s)

  • Bias, Large sample asymptotic, Mean squared error, Monte-Carlo simulation, Quadratic loss, Risk, Seemingly unrelated regression