題目：On the Sparsity of Mallows Model Averaging Estimator

報告人：劉慶豐 教授

時間：2020年1月8日（周三）10:30－12:00

地點：明德主樓623會議室

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）

報告人簡介：劉慶豐，日本國立小樽商科大學教授，日本京都大學經濟研究所訪問教授。2007年獲得日本京都大學經濟學博士，2008年在美國普林斯頓大學做博士后研究。研究領域為計量經濟理論與方法，研究成果發表Econometrics Journal, Econometric Reviews, Mathematics and Computers in Simulation等多個國際專業雜志。

中國人民大學經濟學院

數量經濟學教研室

2019年12月23日

編輯:楊菲 核稿:章永輝