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Identification Of Discrete Choice Dynamic Programming Models With Nonparametric Distribution Of Unobservables

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  • Chen, Le-Yu

Abstract

This paper presents semiparametric identification results for the Rust (1994) class of discrete choice dynamic programming (DCDP) models. We develop sufficient conditions for identification of the deep structural parameters for the case where the per-period utility function ascribed to one choice in the model is parametric but the distribution of unobserved state variables is nonparametric. The proposed identification strategy does not rely on availability of the terminal period data and can therefore be applied to infinite horizon structural dynamic models. Identifying power comes from assuming that the agent’s per-period utilities admit continuous choice-specific state variables that are observed with sufficient variation and satisfy certain conditional independence assumptions on the joint time series of observables. These conditions allow us to formulate exclusion restrictions for identifying the primitive structural functions of the model.

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  • Chen, Le-Yu, 2017. "Identification Of Discrete Choice Dynamic Programming Models With Nonparametric Distribution Of Unobservables," Econometric Theory, Cambridge University Press, vol. 33(3), pages 551-577, June.
    • Handle: RePEc:cup:etheor:v:33:y:2017:i:03:p:551-577_00
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    Cited by:

    1. Norets, Andriy & Shimizu, Kenichi, 2024. "Semiparametric Bayesian estimation of dynamic discrete choice models," Journal of Econometrics, Elsevier, vol. 238(2).
    2. Higgins, Ayden & Jochmans, Koen, 2023. "Identification of mixtures of dynamic discrete choices," Journal of Econometrics, Elsevier, vol. 237(1).
    3. Myrto Kalouptsidi & Paul T. Scott & Eduardo Souza‐Rodrigues, 2021. "Identification of counterfactuals in dynamic discrete choice models," Quantitative Economics, Econometric Society, vol. 12(2), pages 351-403, May.
    4. Victor Aguirregabiria & Allan Collard-Wexler & Stephen P. Ryan, 2021. "Dynamic Games in Empirical Industrial Organization," Papers 2109.01725, arXiv.org, revised Sep 2021.
    5. Buchholz, Nicholas & Shum, Matthew & Xu, Haiqing, 2021. "Semiparametric estimation of dynamic discrete choice models," Journal of Econometrics, Elsevier, vol. 223(2), pages 312-327.
    6. Erhao Xie, 2022. "Nonparametric Identification of Incomplete Information Discrete Games with Non-equilibrium Behaviors," Staff Working Papers 22-22, Bank of Canada.
    7. Komarova, Tatiana & Sanches, Fábio Adriano & Silva Junior, Daniel & Srisuma, Sorawoot, 2018. "Joint analysis of the discount factor and payoff parameters in dynamic discrete choice games," LSE Research Online Documents on Economics 86858, London School of Economics and Political Science, LSE Library.
    8. Schiraldi, Pasquale & Levy, Matthew R., 2020. "Identification of intertemporal preferences in history-dependent dynamic discrete choice models," CEPR Discussion Papers 14447, C.E.P.R. Discussion Papers.
    9. Kalouptsidi, Myrto & Souza-Rodrigues, Eduardo & Scott, Paul, 2017. "Identification of Counterfactuals in Dynamic Discrete Choice Models," CEPR Discussion Papers 12470, C.E.P.R. Discussion Papers.
    10. Schneider, Ulrich, 2019. "Identification of Time Preferences in Dynamic Discrete Choice Models: Exploiting Choice Restrictions," MPRA Paper 102137, University Library of Munich, Germany, revised 29 Jul 2020.

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