Least Squares Model Combining by Mallows Criterion

Abstract

This note is in response to a recent paper by Hansen (2007, Econometrica) who proposed an optimal model average estimator with weights selected by minimizing a Mallows criterion. The main contribution of Hansen's paper is a demonstration that the Mallows criterion is asymptotically equivalent to the squared error, so the model average estimator that minimizes the Mallows criterion also minimizes the squared error in large samples. We are concerned with two assumptions that accompany Hansen's approach. First is the assumption that the approximating models are strictly nested in a way that depends on the ordering of regressors. Often there is no clear basis for the ordering and the approach does not permit non-nested models which are more realistic in a practical sense. Second, for the optimality result to hold the model weights are required to lie within a special discrete set. In fact, Hansen (2007) noted both difficulties and called for extensions of the proof techniques. We provide an alternative proof which shows that the result on the optimality of the Mallows criterion in fact holds for continuous model weights and under a non-nested set-up that allows any linear combination of regressors in the approximating models that make up the model average estimator. These are important extensions and our results provide a stronger theoretical basis for the use of the Mallows criterion in model averaging by strengthening existing findings.

Keywords: asymptotic optimality, continuous weights, Mallows criterion, non-nested models

JEL Classification: C51, C52