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Semi-Parametric Inference in Dynamic Binary Choice Models

Author

Listed:
  • Andriy Norets

    () (Department of Economics, Princeton University)

  • Xun Tang

    () (Department of Economics, University of Pennsylvania)

Abstract

We introduce an approach for semi-parametric 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 of 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, 2013. "Semi-Parametric Inference in Dynamic Binary Choice Models," PIER Working Paper Archive 13-054, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
  • Handle: RePEc:pen:papers:13-054
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    References listed on IDEAS

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    1. Rust, John, 1987. "Optimal Replacement of GMC Bus Engines: An Empirical Model of Harold Zurcher," Econometrica, Econometric Society, vol. 55(5), pages 999-1033, September.
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    5. V. Joseph Hotz & Robert A. Miller, 1993. "Conditional Choice Probabilities and the Estimation of Dynamic Models," Review of Economic Studies, Oxford University Press, vol. 60(3), pages 497-529.
    6. Aguirregabiria, Victor & Mira, Pedro, 2010. "Dynamic discrete choice structural models: A survey," Journal of Econometrics, Elsevier, vol. 156(1), pages 38-67, May.
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    10. Arie Beresteanu & Francesca Molinari, 2008. "Asymptotic Properties for a Class of Partially Identified Models," Econometrica, Econometric Society, vol. 76(4), pages 763-814, July.
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    13. Canay, Ivan A., 2010. "EL inference for partially identified models: Large deviations optimality and bootstrap validity," Journal of Econometrics, Elsevier, vol. 156(2), pages 408-425, June.
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    Citations

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    Cited by:

    1. Antonio Merlo & Xun Tang, 2010. "Identification and Estimation of Stochastic Bargaining Models, Third Version," PIER Working Paper Archive 11-008, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 11 Mar 2011.
    2. 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.
    3. Antonio Merlo & Xun Tang, 2011. "Identification and Estimation of Stochastic Bargaining Models, Fourth Version," PIER Working Paper Archive 11-035, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 19 Oct 2011.
    4. Li, Yong & Liu, Xiaobin & Zeng, Tao & Yu, Jun, 2018. "A Posterior-Based Wald-Type Statistic for Hypothesis Testing," Economics and Statistics Working Papers 8-2018, Singapore Management University, School of Economics.

    More about this item

    Keywords

    Dynamic discrete choice models; Markov decision processes; semi-parametric inference; identification; Bayesian estimation; MCMC;

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

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