中国人民大学经济学院 数量经济学Seminar  
题目：Generalized least squares model averaging
报告人：刘庆丰 教授

时间：2016年3月18日（周五）10:30－12:00

地点：明德主楼734会议室

       
 
Abstract: In this article, we propose a method of averaging generalized least
squares estimators for linear regression models with heteroskedastic
errors. The averaging weights are chosen to minimize Mallows’ Cp-like
criterion. We show that the weight vector selected by our method is
optimal. It is also shown that this optimality holds even when the
variances of the error terms are estimated and the feasible
generalized least squares estimators are averaged. The variances can
be estimated parametrically or nonparametrically. Monte Carlo
simulation results are encouraging. An empirical example illustrates
that the proposed method is useful for predicting a measure of firms’
performance.
 
报告人简介：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，研究成果发表在Econometrics Journal, Econometric Reviews, Mathematics and Computers in Simulation等多个国际专业杂志。
 
 
 
中国人民大学经济学院 数量经济学教研室 2016年3月11日