This paper considers the problem of selection of weights for averaging across leastsquares estimates obtained from a set of models. Existing model average methods are based on exponential AIC and BIC weights. In distinction, this paper proposes selecting the weights by minimizing a Mallows ’ criterion, the latter an estimate of the average squared error from the model average fit. We show that our new Mallows ’ Model Average (MMA) estimator is asymptotically optimal in the sense of achieving the lowest possible squared error in a class of discrete model average estimators. In a simulation experiment we show that the MMA estimator compares favorably with those based on AIC and BIC weights. The proof of the main result is an application of Li (1987). Research supported by the National Science Foundation. I gratefully thank the Co-Editor (Whitney Newey), three referees, and Benedickt Potscher for helpful comments.

This essay examines the problem of inference within a rational expectations model from two perspectives: that of an econometrician and that of the economic agents within the model. The assumption of rational expectations has been and remains an important component to quantitative research. It endows economic decision makers with knowledge of the probability law implied by the economic model. As such, it is an equilibrium concept. Imposing rational expectations removed from consideration the need for separately specifying beliefs or subjective components of uncertainty. Thus, it simplified model specification and implied an array of testable implications that are different from those considered previously. It reframed policy analysis by questioning the effectiveness of policy levers that induce outcomes that differ systematically from individual beliefs.