Abstract
Selecting a good estimate for a constricted linear regression model is investigated by using the generalized information criterion. Some asymptotic properties of the selection procedure with the model average technique are established. It is shown that the selection procedure is asymptotically efficient in the sense that a fitted estimate asymptotically obtains the minimum average squared error from a class of model average estimators.
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