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Identification of Dynamic Panel Logit Models with Fixed Effects

Author

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  • Christopher Dobronyi
  • Jiaying Gu
  • Kyoo il Kim

Abstract

We show that the identification problem for a class of dynamic panel logit models with fixed effects has a connection to the truncated moment problem in mathematics. We use this connection to show that the sharp identified set of the structural parameters is characterized by a set of moment equality and inequality conditions. This result provides sharp bounds in models where moment equality conditions do not exist or do not point identify the parameters. We also show that the sharp identifying content of the non-parametric latent distribution of the fixed effects is characterized by a vector of its generalized moments, and that the number of moments grows linearly in T. This final result lets us point identify, or sharply bound, specific classes of functionals, without solving an optimization problem with respect to the latent distribution.

Suggested Citation

  • Christopher Dobronyi & Jiaying Gu & Kyoo il Kim, 2021. "Identification of Dynamic Panel Logit Models with Fixed Effects," Papers 2104.04590, arXiv.org, revised Apr 2021.
    • Handle: RePEc:arx:papers:2104.04590
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    References listed on IDEAS

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    1. Victor Aguirregabiria & Jesus M. Carro, 2021. "Identification of Average Marginal Effects in Fixed Effects Dynamic Discrete Choice Models," Papers 2107.06141, arXiv.org, revised Jul 2024.
    2. Bo E. Honor'e & Martin Weidner, 2020. "Moment Conditions for Dynamic Panel Logit Models with Fixed Effects," Papers 2005.05942, arXiv.org, revised Dec 2023.
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    4. Arellano, Manuel & Honore, Bo, 2001. "Panel data models: some recent developments," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 5, chapter 53, pages 3229-3296, Elsevier.
    5. Bo E. Honoré & Elie Tamer, 2006. "Bounds on Parameters in Panel Dynamic Discrete Choice Models," Econometrica, Econometric Society, vol. 74(3), pages 611-629, May.
    6. Gary Chamberlain, 1980. "Analysis of Covariance with Qualitative Data," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 47(1), pages 225-238.
    7. Aguirregabiria, Victor & Gu, Jiaying & Luo, Yao, 2021. "Sufficient statistics for unobserved heterogeneity in structural dynamic logit models," Journal of Econometrics, Elsevier, vol. 223(2), pages 280-311.
    8. Hahn, Jinyong, 2001. "The Information Bound Of A Dynamic Panel Logit Model With Fixed Effects," Econometric Theory, Cambridge University Press, vol. 17(5), pages 913-932, October.
    9. Victor Chernozhukov & Iván Fernández‐Val & Jinyong Hahn & Whitney Newey, 2013. "Average and Quantile Effects in Nonseparable Panel Models," Econometrica, Econometric Society, vol. 81(2), pages 535-580, March.
    10. Buchinsky, Moshe & Hahn, Jinyong & Kim, Kyoo il, 2010. "Semiparametric information bound of dynamic discrete choice models," Economics Letters, Elsevier, vol. 108(2), pages 109-112, August.
    11. Bo E. Honoré & Ekaterini Kyriazidou, 2000. "Panel Data Discrete Choice Models with Lagged Dependent Variables," Econometrica, Econometric Society, vol. 68(4), pages 839-874, July.
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    Citations

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    Cited by:

    1. Victor Aguirregabiria & Jesus M. Carro, 2021. "Identification of Average Marginal Effects in Fixed Effects Dynamic Discrete Choice Models," Papers 2107.06141, arXiv.org, revised Jul 2024.
    2. Kevin Dano, 2023. "Transition Probabilities and Moment Restrictions in Dynamic Fixed Effects Logit Models," Papers 2303.00083, arXiv.org, revised Dec 2023.
    3. Cavit Pakel & Martin Weidner, 2023. "Bounds on Average Effects in Discrete Choice Panel Data Models," Papers 2309.09299, arXiv.org, revised May 2024.
    4. Senay Sokullu & Irene Botosaru & Chris Muris, 2022. "Time-Varying Linear Transformation Models with Fixed Effects and Endogeneity for Short Panels," Bristol Economics Discussion Papers 22/756, School of Economics, University of Bristol, UK.
    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 Aug 2024.
    6. Geert Dhaene & Martin Weidner, 2023. "Approximate Functional Differencing," Papers 2301.13736, arXiv.org, revised May 2023.
    7. Irene Botosaru & Chris Muris, 2022. "Identification of time-varying counterfactual parameters in nonlinear panel models," Papers 2212.09193, arXiv.org, revised Nov 2023.

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