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Preliminary-Test Estimation of the Error Variance in Linear Regression

We derive exact finite-sample expressions for the biases and risks of several common pretest estimators of the scale parameter in the linear regression model. These estimators are associated with least squares, maximum likelihood and minimum mean squared error component estimators. Of these three criteria, the last is found to be superior (in terms of risk under quadratic loss) when pretesting in typical situations.
Year of publication:
1987
Authors: Clarke, Judith A. ; Giles, David E. A. ; Wallace, T. Dudley
Published in:
Econometric Theory. - Cambridge University Press. - Vol. 3.1987, 02, p. 299-304
Publisher: Cambridge University Press
Description of contents: Abstract [journals.cambridge.org]
Type of publication: Article
Source:
Persistent link: https://www.econbiz.de/10005104634
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