题目：On the Sparsity of Mallows Model Averaging Estimator

报告人：刘庆丰 教授

时间：2020年1月8日（周三）10:30－12:00

地点：明德主楼623会议室

Abstract:

We show that Mallows model averaging estimator proposed by Hansen (2007) can be written as a least squares estimation with a weighted L1 penalty and additional constraints. By exploiting this representation, we demonstrate that the weight vector obtained by this model averaging procedure has a sparsity property in the sense that a subset of models receives exactly zero weights. Moreover, this representation allows us to adapt algorithms developed to efficiently solve minimization problems with many parameters and weighted L1 penalty. In particular, we develop a new coordinate-wise descent algorithm for model averaging. Simulation studies show that the new algorithm computes the model averaging estimator much faster and requires less memory than conventional methods when there are many models.（With Yang Feng and Okui Ryo）

报告人简介：刘庆丰，日本国立小樽商科大学教授，日本京都大学经济研究所访问教授。2007年获得日本京都大学经济学博士，2008年在美国普林斯顿大学做博士后研究。研究领域为计量经济理论与方法，研究成果发表Econometrics Journal, Econometric Reviews, Mathematics and Computers in Simulation等多个国际专业杂志。

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编辑:杨菲 核稿:章永辉