Presenter: Ji-Liang
Shui
Affiliation: Assistant Professor, Hanqing
Advanced Institute of Economics and Finance, Renmin University
Topic: Identification
and Estimation of Semi-parametric Censored Dynamic Panel Data
Models
Abstract
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).
Time: 16:00-17:30,
Nov 22nd, 2013
Location: General
Room 2, 2nd Floor, Zhongcai Building, CUFE
