On small sample properties of the mixed regression predictor under misspecification

This paper relaxes the Mittelhammer's (1981) assumption that the value of the true variance is known in the mixed regression model and examines the small sample, properties of the feasible mixed regression predictor under misspecification. The paper shows that the feasible mixed regression predictor is not always superior to the ordinary least squares predictor in terms of the weak mean square error when there exist omitted variables in the model. Further it shows that misspecificstion works favorably for the ordinary least squares predictor.