Operational variants of the minimum mean squared error estimator in linear regression models with non-spherical disturbances

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)332-342
Journal / PublicationAnnals of the Institute of Statistical Mathematics
Issue number2
Publication statusPublished - 2000


There is a good deal of literature that investigates the properties of various operational variants of Theil's (1971, Principles of Econometrics, Wiley, New York) minimum mean squared error estimator. It is interesting that virtually all of the existing analysis to date is based on the premise that the model's disturbances are i.i.d., an assumption which is not satisfied in many practical situations. In this paper, we consider a model with non-spherical errors and derive the asymptotic distribution, bias and mean squared error of a general class of feasible minimum mean squared error estimators. A Monte-Carlo experiment is conducted to examine the performance of this class of estimators in finite samples.

Research Area(s)

  • Asymptotic expansion, Minimum mean squared error, Quadratic loss, Risk, Stein-rule