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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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    More about this item

    Keywords

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

    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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