Integrated likelihood based inference for nonlinear panel data models with unobserved effects

Martin Schumann, Thomas A. Severini and Gautam Tripathi

Journal of Econometrics, 2021, vol. 223, issue 1, 73-95

Abstract: We propose a new integrated likelihood based approach for estimating panel data models when the unobserved individual effects enter the model nonlinearly. Unlike existing integrated likelihoods in the literature, the one we propose is closer to a genuine likelihood. Although the statistical theory for the proposed estimator is developed in an asymptotic setting where the number of individuals and the number of time periods both approach infinity, results from a simulation study suggest that our methodology can work very well even in moderately sized panels of short duration in both static and dynamic models.

Keywords: Fixed effects; Integrated likelihood; Nonlinear models; Panel data (search for similar items in EconPapers)
JEL-codes: C23 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Working Paper: Integrated Likelihood Based Inference for Nonlinear Panel Data Models with Unobserved Effects (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:223:y:2021:i:1:p:73-95

DOI: 10.1016/j.jeconom.2020.10.001

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