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Transition Probabilities and Moment Restrictions in Dynamic Fixed Effects Logit Models

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  • Kevin Dano

Abstract

Dynamic logit models are popular tools in economics to measure state dependence. This paper introduces a new method to derive moment restrictions in a large class of such models with strictly exogenous regressors and fixed effects. We exploit the common structure of logit-type transition probabilities and elementary properties of rational fractions, to formulate a systematic procedure that scales naturally with model complexity (e.g the lag order or the number of observed time periods). We detail the construction of moment restrictions in binary response models of arbitrary lag order as well as first-order panel vector autoregressions and dynamic multinomial logit models. Identification of common parameters and average marginal effects is also discussed for the binary response case. Finally, we illustrate our results by studying the dynamics of drug consumption amongst young people inspired by Deza (2015).

Suggested Citation

  • Kevin Dano, 2023. "Transition Probabilities and Moment Restrictions in Dynamic Fixed Effects Logit Models," Papers 2303.00083, arXiv.org, revised Dec 2023.
    • Handle: RePEc:arx:papers:2303.00083
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. Bryan S. Graham, 2016. "Homophily and transitivity in dynamic network formation," CeMMAP working papers 16/16, Institute for Fiscal Studies.
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    6. 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.
    7. Chris Muris & Pedro Raposo & Sotiris Vandoros, 2020. "A Dynamic Ordered Logit Model with Fixed Effects," Department of Economics Working Papers 2020-14, McMaster University.
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    10. Bo E. Honoré & Martin Weidner, 2021. "Moment Conditions for Dynamic Panel Logit Models with Fixed Effects," Working Papers 2021-79, Princeton University. Economics Department..
    11. Deza, Monica, 2015. "Is there a stepping stone effect in drug use? Separating state dependence from unobserved heterogeneity within and between illicit drugs," Journal of Econometrics, Elsevier, vol. 184(1), pages 193-207.
    12. Magnac, Thierry, 2000. "Subsidised Training and Youth Employment: Distinguishing Unobserved Heterogeneity from State Dependence in Labour Market Histories," Economic Journal, Royal Economic Society, vol. 110(466), pages 805-837, October.
    13. Bo E. Honoré & Chris Muris & Martin Weidner, 2021. "Dynamic Ordered Panel Logit Models," Working Papers 2021-14, Princeton University. Economics Department..
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    17. 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.
    18. Bo E. Honoré & Luojia Hu & Ekaterini Kyriazidou & Martin Weidner, 2023. "Simultaneity in binary outcome models with an application to employment for couples," Empirical Economics, Springer, vol. 64(6), pages 3197-3233, June.
    19. Egger, Peter & Pfaffermayr, Michael & Weber, Andrea, 2003. "Sectoral Adjustment of Employment: The Impact of Outsourcing and Trade at the Micro Level," IZA Discussion Papers 921, Institute of Labor Economics (IZA).
    20. Yoshitsugu Kitazawa, 2013. "Exploration of dynamic fixed effects logit models from a traditional angle," Discussion Papers 60, Kyushu Sangyo University, Faculty of Economics.
    21. 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.
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    Cited by:

    1. St'ephane Bonhomme & Kevin Dano, 2023. "Functional Differencing in Networks," Papers 2307.11484, arXiv.org.
    2. Andrew Chesher & Adam Rosen & Yuanqi Zhang, 2024. "Robust analysis of short panels," IFS Working Papers WCWP01/24, Institute for Fiscal Studies.

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