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

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  • Schumann, Martin
  • Severini, Thomas A.
  • Tripathi, Gautam

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.

Suggested Citation

  • Schumann, Martin & Severini, Thomas A. & Tripathi, Gautam, 2021. "Integrated likelihood based inference for nonlinear panel data models with unobserved effects," Journal of Econometrics, Elsevier, vol. 223(1), pages 73-95.
    • Handle: RePEc:eee:econom:v:223:y:2021:i:1:p:73-95
    DOI: 10.1016/j.jeconom.2020.10.001
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    Cited by:

    1. Schumann, Martin & Severini, Thomas A. & Tripathi, Gautam, 2023. "The role of score and information bias in panel data likelihoods," Journal of Econometrics, Elsevier, vol. 235(2), pages 1215-1238.
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    4. Marta F. Arroyabe & Martin Schumann, 2022. "On the Estimation of True State Dependence in the Persistence of Innovation," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 84(4), pages 850-893, August.
    5. Li, Libo & Yu, Huan & Kunc, Martin, 2024. "The impact of forum content on data science open innovation performance: A system dynamics-based causal machine learning approach," Technological Forecasting and Social Change, Elsevier, vol. 198(C).

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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

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