主 题：Identification and Estimation of Semi-parametric Censored Dynamic Panel Data Models
摘 要
This study presents a semiparametric identification and estimation method for censored dynamic panel data models and their average partial effects using only two-period data. The proposed method transforms the semi-parametric specification of censored dynamic panel data models into a valid semi-parametric family of PDFs of observables without modeling the distribution of the initial condition. Then the censored dynamic panel data models can be identified by a standard maximum likelihood estimation (MLE). The identifying assumptions are related to the completeness of the families of known semiparametric PDFs corresponding to censored dynamic panel data models and observed conditional density functions between the dependent and explanatory variables. This study shows that the families of PDFs corresponding to dynamic tobit models and dynamic lognormal hurdle models satisfy the identification assumptions with two types of data generating process (DGP). This study proposes a sieve maximum likelihood estimator (sieve MLE) and investigates the finite sample properties of these sieve-based estimators through Monte Carlo analysis. This study presents the dynamic behavior of annual individual health expenditures estimated as an empirical illustration using the dynamic tobit model and data from the Medical Expenditure Panel Survey (MEPS).
主讲人：徐吉良博士现任人民大学汉青高级经济与金融研究院助理教授，2009年在John Hopkins University获得经济学博士学位，2004年在Indiana University at Bloomington获得数学博士学位，主要研究领域包括微观和应用计量经济学、劳动经济学等。研究成果发表在Journal of Econometrics等。
时 间：2013年11月22日星期五16:00-17:30
地 点：中财大厦二层通用教室2
主办单位：人力资本与劳动经济研究中心