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Semiparametric Inference in Dynamic Binary Choice Models, Second Version

Author

Listed:
  • Andriy Norets

    () (Department of Economics, Princeton University)

  • Xun Tang

    () (Department of Economics, University of Pennsylvania)

Abstract

We introduce an approach for semiparametric inference in dynamic binary choice models that does not impose distributional assumptions on the state variables unobserved by the econometrician. The proposed framework combines Bayesian inference with partial identification results. The method is applicable to models with finite space for observed states. We demonstrate the method on Rust's model of bus engine replacement. The estimation experiments show that the parametric assumptions about the distribution of the unobserved states can have a considerable effect on the estimates of per-period payoffs. At the same time, the effect of these assumptions on counterfactual conditional choice probabilities can be small for most of the observed states.

Suggested Citation

  • Andriy Norets & Xun Tang, 2010. "Semiparametric Inference in Dynamic Binary Choice Models, Second Version," PIER Working Paper Archive 12-017, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 17 Apr 2012.
  • Handle: RePEc:pen:papers:12-017
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    Cited by:

    1. Xiaohong Chen & Timothy Christensen & Keith O'Hara & Elie Tamer, 2016. "MCMC Confidence sets for Identified Sets," Cowles Foundation Discussion Papers 2037R, Cowles Foundation for Research in Economics, Yale University, revised Jul 2016.
    2. Victor Aguirregabiria & Arvind Magesan, "undated". "Identification and Estimation of Dynamic Games when Players' Beliefs Are Not in Equilibrium," Working Papers 2012-03, Department of Economics, University of Calgary.
    3. Xiaohong Chen & Timothy M. Christensen & Elie Tamer, 2017. "Monte Carlo confidence sets for identified sets," CeMMAP working papers CWP43/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    4. Xiaohong Chen & Timothy M. Christensen & Keith O'Hara & Elie Tamer, 2016. "MCMC confidence sets for identified sets," CeMMAP working papers CWP28/16, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    5. Abbring, Jaap H & Daljord, Øystein, 2016. "Identifying the Discount Factor in Dynamic Discrete Choice Models," CEPR Discussion Papers 11133, C.E.P.R. Discussion Papers.
    6. Yuan Liao & Anna Simoni, 2012. "Semi-parametric Bayesian Partially Identified Models based on Support Function," Papers 1212.3267, arXiv.org, revised Nov 2013.
    7. Xiaohong Chen & Timothy Christensen & Elie Tamer, 2016. "MCMC Confidence sets for Identified Sets," Cowles Foundation Discussion Papers 2037, Cowles Foundation for Research in Economics, Yale University.
    8. Mehmet Soytas & Limor Golan & George-Levi Gayle, 2014. "What Accounts for the Racial Gap in Time Allocation and Intergenerational Transmission of Human Capital?," 2014 Meeting Papers 83, Society for Economic Dynamics.
    9. Khai Xiang Chiong & Alfred Galichon & Matt Shum, 2016. "Duality in dynamic discrete‐choice models," Quantitative Economics, Econometric Society, vol. 7(1), pages 83-115, March.
    10. Xiaohong Chen & Timothy Christensen & Elie Tamer, 2016. "Monte Carlo Confidence sets for Identified Sets," Cowles Foundation Discussion Papers 2037R2, Cowles Foundation for Research in Economics, Yale University, revised Sep 2017.
    11. Victor Aguirregabiria & Junichi Suzuki, 2014. "Identification and counterfactuals in dynamic models of market entry and exit," Quantitative Marketing and Economics (QME), Springer, vol. 12(3), pages 267-304, September.
    12. Mira Frick & Ryota Iijima & Tomasz Strzalecki, 2017. "Dynamic Random Utility," Cowles Foundation Discussion Papers 2092, Cowles Foundation for Research in Economics, Yale University.
    13. Liao, Yuan & Simoni, Anna, 2012. "Semi-parametric Bayesian Partially Identified Models based on Support Function," MPRA Paper 43262, University Library of Munich, Germany.
    14. 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.
    15. Myrto Kalouptsidi & Paul T. Scott & Eduardo Souza-Rodrigues, 2015. "Identification of Counterfactuals in Dynamic Discrete Choice Models," NBER Working Papers 21527, National Bureau of Economic Research, Inc.
    16. Myrto Kalouptsidi & Paul T. Scott & Eduardo Souza-Rodrigues, 2015. "Identification of Counterfactuals and Payoffs in Dynamic Discrete Choice with an Application to Land Use," Working Papers tecipa-546, University of Toronto, Department of Economics.
    17. Xiaohong Chen & Timothy Christensen & Elie Tamer, 2016. "Monte Carlo Confidence Sets for Identified Sets," Papers 1605.00499, arXiv.org, revised Sep 2017.
    18. Yuan Liao & Anna Simoni, 2016. "Bayesian Inference for Partially Identified Convex Models: Is it Valid for Frequentist Inference?," Departmental Working Papers 201607, Rutgers University, Department of Economics.
    19. Raffaella Giacomini & Toru Kitagawa & Alessio Volpicella, 2017. "Uncertain identification," CeMMAP working papers CWP18/17, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.

    More about this item

    Keywords

    Dynamic discrete choice models; Markov decision processes; dynamic games; semiparametric inference; identification; Bayesian estimation; MCMC;

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities

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