Estimation of the Error Variance after a Preliminary-Test of Homogeneity in a Regression Model with Spherically Symmetric Disturbances

In this paper we consider the risk (under quadratic loss) of an estimator cf the error variance after a pre-test for homogeneity of the variances in the two-sample linear regression model. We investigate the effects on risk of assuming normal disturbances when in fact the error distribution is spherically symmetric. We also broaden the standard assumption that the never-pool variance estimators are based on the least squares principle. Using the special case of multivariate Student-t regression disturbances as an illustration, our results show that in some situations we should always pre-test, even if the error variances are equal, and we provide the optimal test critical value. The evaluations also show that using the least squares technique to form the never-pool estimators may not always be the preferred strategy.

Issue Date:
Jul 07 1990
Publication Type:
Working or Discussion Paper
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 Record created 2017-09-11, last modified 2018-01-23

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