Preliminary-Test Estimation of the Error Variance in Linear Regression
Abstract
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.
Type
Journal articlePermalink
https://hdl.handle.net/10161/2568Collections
More Info
Show full item recordScholars@Duke
Thomas D. Wallace
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.
Articles written by Duke faculty are made available through the campus open access policy. For more information see: Duke Open Access Policy
Rights for Collection: Scholarly Articles