主 题：Estimation for Dynamic and Static Panel Probit Models with Large Individual Effects
主讲人：高巍教授
主持人：马铁丰教授
时 间：2015年5月6日上午10:30-11:30
地 点：通博楼B座212学术会议室
主办单位：统计研究中心 统计学院 科研处
主讲人简介：
东北师范大学数学与统计学院教授、博士生导师，先后获得留学归国基金、国家自然科学基金、教育部新世纪人才支持计划项目（2011.1-2013.12）的资助，参与多项国家自然科学基金。先后访问日本数理统计研究所、美国密苏里大学（University of Missouri）、英国伦敦政治经济学院、香港大学、香港中文大学等。主要的学术贡献是将目前广泛应用于统计学、信息学、计算机科学上GIS(Generalized Iterative Scaling)方法扩展到更一般的情形，提出了UGIS(Unified Generalized Iterative Scaling)方法。
内容提要：
For discrete panel data, the dynamic relationship between successive observations is often of interest. We consider a dynamic probit model for short panel data. A problem with estimating the dynamic parameter of interest is that the model contains a large number of nuisance parameters, one for each individual. Heckman proposed to use maximum likelihood estimation of the dynamic parameter, which, however, does not perform well if the individual effects are large.
We suggest new estimators for the dynamic parameter, based on the assumption that the individual parameters are random and possibly large.Theoretical properties of our estimators are derived and a simulation study shows they have some advantages compared to Heckman's estimator.
