Pre-testing for Linear Restrictions in a Regression Model with Spherially Symmetric Disturbances

In this paper we derive the exact risk (under quadratic loss) of pretest estimators of the prediction vector and of the error variance of a linear regression model with spherically symmetric disturbances. The pre-test in question is one of the validity of a set of exact linear restrictions on the model's coefficient vector. We demonstrate how the known results for the model with normal disturbances can be extended to this broader case. Numerical evaluations of the risk expressions in the particular case of multivariate Student-t errors suggest that sampling properties of these pre-test estimators under these conditions are qualitatively similar to those which apply under normal errors.

Issue Date:
Jan 01 1989
Publication Type:
Working or Discussion Paper
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 Record created 2017-09-11, last modified 2020-10-28

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