Integrated Likelihood Based Inference for Nonlinear Panel Data Models with Unobserved Effects

Martin Schumann (), Thomas A. Severini () and Gautam Tripathi
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Martin Schumann: CREA, Université du Luxembourg
Thomas A. Severini: Northwestern University, Evanston, USA

DEM Discussion Paper Series from Department of Economics at the University of Luxembourg

Abstract: Panel data models with fixed effects are widely used by economists and other social scientists to capture the effects of unobserved individual heterogeneity. In this paper, 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 C33 C55 (search for similar items in EconPapers)
Date: 2017
New Economics Papers: this item is included in nep-ecm and nep-knm
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https://hdl.handle.net/10993/29663 (application/pdf)

Related works:
Journal Article: Integrated likelihood based inference for nonlinear panel data models with unobserved effects (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:luc:wpaper:17-01

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