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Estimation for Dynamic and Static Panel Probit Models with Large Individual Effects
Author(s) -
Gao Wei,
Bergsma Wicher,
Yao Qiwei
Publication year - 2017
Publication title -
journal of time series analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.576
H-Index - 54
eISSN - 1467-9892
pISSN - 0143-9782
DOI - 10.1111/jtsa.12178
Subject(s) - estimator , mathematics , probit , probit model , econometrics , panel data , nuisance parameter , random effects model , statistics , maximum likelihood , estimation theory , estimation , economics , medicine , meta analysis , management
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 with Heckman's estimator and the modified profile likelihood estimator for fixed effects.