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Identification of Counterfactuals in Dynamic Discrete Choice Models

Author

Listed:
  • Paul Scott

    (Toulouse School of Economics)

  • Eduardo Souza-Rodrigues

    (University of Toronto)

  • Myrto Kalouptsidi

    (Princeton)

Abstract

Dynamic discrete choice models are non-parametrically not identified without restrictions on payoff functions, but counterfactual behavior is sometimes identified even when payoffs are not. We provide necessary and sufficient conditions for the identification behavior and welfare for a broad class of counterfactuals. These conditions are easily checked and can be used to establish the identification (or non-identification) of virtually every counterfactual in the dynamic discrete choice literature. We illustrate the theoretical findings with a numerical model of a monopolist's entry/exit problem and an empirical model of agricultural land use. For each application, we provide relevant examples of both identified and non-identified counterfactuals.

Suggested Citation

  • Paul Scott & Eduardo Souza-Rodrigues & Myrto Kalouptsidi, 2016. "Identification of Counterfactuals in Dynamic Discrete Choice Models," 2016 Meeting Papers 282, Society for Economic Dynamics.
    • Handle: RePEc:red:sed016:282
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    Cited by:

    1. Jaap H. Abbring & {O}ystein Daljord, 2018. "Identifying the Discount Factor in Dynamic Discrete Choice Models," Papers 1808.10651, arXiv.org, revised Sep 2019.
    2. Kalouptsidi, Myrto & Papageorgiou, Theodore & Brancaccio, Giulia, 2017. "Geography, Search Frictions and Endogenous Trade Costs," CEPR Discussion Papers 12141, C.E.P.R. Discussion Papers.
    3. Mitsuru Igami, 2018. "Industry Dynamics of Offshoring: The Case of Hard Disk Drives," American Economic Journal: Microeconomics, American Economic Association, vol. 10(1), pages 67-101, February.
    4. Patrick Bajari & Chenghuan Sean Chu & Denis Nekipelov & Minjung Park, 2016. "Identification and semiparametric estimation of a finite horizon dynamic discrete choice model with a terminating action," Quantitative Marketing and Economics (QME), Springer, vol. 14(4), pages 271-323, December.
    5. Jason R. Blevins & Wei Shi & Donald R. Haurin & Stephanie Moulton, 2020. "A Dynamic Discrete Choice Model Of Reverse Mortgage Borrower Behavior," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 61(4), pages 1437-1477, November.
    6. 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.
    7. Kalouptsidi, Myrto & Scott, Paul T. & Souza-Rodrigues, Eduardo, 2017. "On the non-identification of counterfactuals in dynamic discrete games," International Journal of Industrial Organization, Elsevier, vol. 50(C), pages 362-371.
    8. De Groote, Olivier, 2019. "Dynamic Effort Choice in High School: Costs and Benefits of an Academic Track," TSE Working Papers 19-1002, Toulouse School of Economics (TSE), revised Jun 2023.

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