Preliminary-Test Estimation of the Error Variance in Linear Regression
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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.
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James B. Duke Distinguished Professor Emeritus of Economics
Professor Wallace’s most recent endeavor was the completion of a textbook covering general knowledge within his field. The book was Econometrics: An Introduction, written in collaboration with his former student, Lew Silver. As a researcher, his investigations explored such variables as human capital accumulation, linear restrictions in regression, time series data, multicollinearity and low-order moments in stable lag distribution, fertility and replacement, full time schooling, the mean squa
This author no longer has a Scholars@Duke profile, so the information shown here reflects their Duke status at the time this item was deposited.