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Estimation for dynamic and static panel probit models with large individual effects

Gao, Wei, Bergsma, Wicher and Yao, Qiwei ORCID: 0000-0003-2065-8486 (2017) Estimation for dynamic and static panel probit models with large individual effects. Journal of Time Series Analysis, 38 (2). pp. 266-284. ISSN 0143-9782

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Identification Number: 10.1111/jtsa.12178


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.

Item Type: Article
Official URL:
Additional Information: © 2016 Wiley
Divisions: Statistics
Subjects: H Social Sciences > HA Statistics
Date Deposited: 27 Jan 2016 11:53
Last Modified: 20 Oct 2021 03:23
Projects: EP/L01226X/1, No. 11471068
Funders: Engineering and Physical Sciences Research Council, National Nature Science Foundation of China

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