Mean square error matrix superiority of the mixed regression estimator under misspecification

Abstract

Conditions are derived under which the mixed regression estimator (MRE) is better then the ordinary least-squares estimator (OLSE) with respect to the mean square error (MSE) matrix criterion especially for the case that the regression model is misspecified. Some attention is paid to prediction, where it is shown that the MRE-predictor is potentially superior to the OLS-predictor under the same criterion.