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

Author

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  • martin Schumann

    (CREA, Université du Luxembourg)

  • Thomas A. Severini

    (Northwestern University, Evanston, USA)

  • Gautam Tripathi

    (CREA, Université du 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.

Suggested Citation

  • martin Schumann & Thomas A. Severini & Gautam Tripathi, 2017. "Integrated Likelihood Based Inference for Nonlinear Panel Data Models with Unobserved Effects," DEM Discussion Paper Series 17-01, Department of Economics at the University of Luxembourg.
  • Handle: RePEc:luc:wpaper:17-01
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    More about this item

    Keywords

    Fixed effects; Integrated likelihood; Nonlinear models; Panel data;
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis

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