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Moment Conditions for Dynamic Panel Logit Models with Fixed Effects

Author

Listed:
  • Bo E. Honoré

    (Institute for Fiscal Studies and Princeton)

  • Martin Weidner

    (Institute for Fiscal Studies and University College London)

Abstract

This paper builds on Bonhomme (2012) to develop a method to systematically construct moment conditions for dynamic panel data logit models with fixed effects. After introducing the moment conditions obtained in this way, we explore their implications for identification and estimation of the model parameters that are common to all individuals, and we find that those common model parameters are estimable at root-n rate for many more dynamic panel logit models than has been appreciated by the existing literature. In the case where the model contains one lagged variable, the moment conditions in Kitazawa (2013, 2016) are transformations of a subset of ours. A GMM estimator that is based on the moment conditions is shown to perform well in Monte Carlo simulations and in an empirical illustration to labor force participation.

Suggested Citation

  • Bo E. Honoré & Martin Weidner, 2020. "Moment Conditions for Dynamic Panel Logit Models with Fixed Effects," CeMMAP working papers CWP38/20, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    • Handle: RePEc:ifs:cemmap:38/20
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    References listed on IDEAS

    as
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    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Kevin Dano, 2023. "Transition Probabilities and Moment Restrictions in Dynamic Fixed Effects Logit Models," Papers 2303.00083, arXiv.org, revised Dec 2023.
    2. Cavit Pakel & Martin Weidner, 2023. "Bounds on Average Effects in Discrete Choice Panel Data Models," Papers 2309.09299, arXiv.org, revised Dec 2023.
    3. Chris Muris & Pedro Raposo & Sotiris Vandoros, 2020. "A dynamic ordered logit model with fixed effects," Papers 2008.05517, arXiv.org.
    4. Bo E. Honor'e & Chris Muris & Martin Weidner, 2021. "Dynamic Ordered Panel Logit Models," Papers 2107.03253, arXiv.org, revised Jun 2023.
    5. Christopher R. Dobronyi & Fu Ouyang & Thomas Tao Yang, 2023. "Revisiting Panel Data Discrete Choice Models with Lagged Dependent Variables," Papers 2301.09379, arXiv.org, revised Mar 2024.
    6. Francesco Bartolucci & Claudia Pigini & Francesco Valentini, 2023. "Conditional inference and bias reduction for partial effects estimation of fixed-effects logit models," Empirical Economics, Springer, vol. 64(5), pages 2257-2290, May.
    7. Fu Ouyang & Thomas Tao Yang, 2022. "Semiparametric Estimation of Dynamic Binary Choice Panel Data Models," Papers 2202.12062, arXiv.org, revised Feb 2024.
    8. Liang Chen & Minyuan Zhang, 2023. "Common Correlated Effects Estimation of Nonlinear Panel Data Models," Papers 2304.13199, arXiv.org.
    9. Bo E Honoré & Áureo de Paula, 2021. "Identification in simple binary outcome panel data models," The Econometrics Journal, Royal Economic Society, vol. 24(2), pages 78-93.
    10. Rui Wang, 2023. "Testing and Identifying Substitution and Complementarity Patterns," Papers 2304.00678, arXiv.org.
    11. Kitazawa, Yoshitsugu, 2022. "Transformations and moment conditions for dynamic fixed effects logit models," Journal of Econometrics, Elsevier, vol. 229(2), pages 350-362.
    12. Geert Dhaene & Martin Weidner, 2023. "Approximate Functional Differencing," Papers 2301.13736, arXiv.org, revised May 2023.
    13. Hugo Kruiniger, 2021. "Root-n-consistent Conditional ML estimation of dynamic panel logit models with fixed effects," Papers 2103.04973, arXiv.org, revised Apr 2021.

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