Research-Estimation for Discrete Dynamic Models with Large Individual Effects

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Estimation for Discrete Dynamic Models with Large Individual Effects

Speaker: GAO Wei(Northeast Normal University)
Time: May 11, 2017, 10:30-11:30
Location: Conference Room 706, Service Center of Scientific Research and Teaching

个人简介：

高巍, 东北师范大学数学与统计学院, 统计学正教授。他1998年7月毕业于吉林大学，获概率论与数理统计专业博士学位, 2001年10月至2003年9月日本数理统计研究所从事博士后研究。他的研究兴趣包括序约束下的统计推断、短面板数据的统计分析和金融统计。高巍教授主要围绕有序数据的统计分析进行了较为系统的研究，提出了一个称之为Unified Generalized Iterative Scaling方法，将目前广泛应用于信息学和计算科学等学科, 为Darroch and Ratcliff (1972) 所提出的Generalized Iterative Scaling方法拓展到一般的情形，相关研究成果发表在Journal of Multivariate Analysis、Computational Statistics and Data Analysis、Annals of the Institute of Statistical Mathematics等国际统计杂志上。他先后访问过日本数理统研究所、University of Missouri、香港大学、香港中文大学计和伦敦政经学院。他先后主持过5个国家自然科学基金项目、教育部新世纪优秀人才支持计划项目、并参与多项国家自然科学基金项目。他(排名第三)获得2006年度高等学校科学技术奖自然科学奖二等奖。

Abstract:

In this talk, we will consider estimating problems for dynamic models with short panel data. The problem with estimating the dynamic parameter of interest is that the model contains a large number of nuisance parameters, one for each individual. Heckman(1978) proposed to use maximum likelihood estimation of the dynamic parameter when the prior distribution of individual parameters is normally distributed, which is named as Heckman's estimator. However, the estimator is sensitive to the assumption of prior distributions for short panel data, which estimating results have big difference with choice of prior distributions. We propose new estimators for the dynamic parameter, theoretical properties of our estimators are derived and a simulation study shows the proposed estimators have some advantages compared to Heckman's estimator and the modified profile likelihood estimator(MPL) for fixed effects.