Least squares model averaging by Mallows criterion

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal

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Detail(s)

Original languageEnglish
Pages (from-to)277-283
Journal / PublicationJournal of Econometrics
Volume156
Issue number2
Publication statusPublished - Jun 2010

Abstract

This paper is in response to a recent paper by Hansen (2007) 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. The 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 from a practical viewpoint. Second, for the optimality result to hold the model weights are required to lie within a special discrete set. In fact, Hansen 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 results provide a stronger theoretical basis for the use of the Mallows criterion in model averaging by strengthening existing findings. © 2009 Elsevier B.V. All rights reserved.

Research Area(s)

  • Asymptotic optimality, Continuous weights, Mallows criterion, Non-nested models

Citation Format(s)

Least squares model averaging by Mallows criterion. / Wan, Alan T.K.; Zhang, Xinyu; Zou, Guohua.

In: Journal of Econometrics, Vol. 156, No. 2, 06.2010, p. 277-283.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal