Estimation for Dynamic and Static Panel Probit Models with Large Individual Effects. (19th January 2016)

• Record Type: Journal Article
• Authors: Gao, Wei; Bergsma, Wicher; Yao, Qiwei
• Other Names: Subba Rao Tata guestEditor.; Tunnicliffe Wilson Granville guestEditor.
• Is Part Of: Journal of time series analysis. Volume 38:Number 2(2017:Mar.)
• Page Start: 266
• Page End: 284
• Publication Date: 2016-01-19
• Subjects: Dynamic probit regression -- generalized linear models -- panel data -- probit models -- static probit regression; Time-series analysis -- Periodicals; 519.232
• DOI: 10.1111/jtsa.12178
• Languages: English
• ISSNs: 0143-9782

Abstract

Abstract : 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.