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[数量经济学Seminar]Model Selection and Model Averaging for Nonlinear Regression Models
发布日期:2017-03-21
中国人民大学经济学院
数量经济学Seminar
 
  
题目:Model Selection and Model Averaging for Nonlinear Regression Models
报告人:刘庆丰 教授
时间:2017年3月23日(周四)15:00-16:00
地点:明德主楼0404
       
 
Abstract: This paper considers the problem of model selection and model averaging for nonlinear regression models. We propose a new information criterion called nonlinear model information criterion (NIC),which is proved to be an asymptotically unbiased estimator of the risk function under nonlinear settings. We also develop a nonlinear model averaging method (NMA) and extend NIC to NICMA criterion, which is the corresponding weight choosing criterion for NMA. By taking account of complexity of model form into the penalty term, NIC and NICMA achieve significant gain of performance. The optimality of NMA, convergence of the selected weight and other theoretical properties are proved. Simulation results show that NIC and NMA lead to relatively lower risks compared with alternative model selection and model averaging methods under most situations.
 
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学理论与方法,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20日
 


 
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