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Semiparametric Inference in Dynamic Binary Choice Models, Second Version

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Author Info

  • 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.

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File URL: http://economics.sas.upenn.edu/system/files/12-017.pdf
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Bibliographic Info

Paper provided by Penn Institute for Economic Research, Department of Economics, University of Pennsylvania in its series PIER Working Paper Archive with number 12-017.

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Length: 54 pages
Date of creation: 14 Apr 2010
Date of revision: 17 Apr 2012
Handle: RePEc:pen:papers:12-017

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Related research

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

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Cited by:
  1. Victor Aguirregabiria & Junichi Suzuki, 2013. "Identification and Counterfactuals in Dynamic Models of Market Entry and Exit," Working Papers tecipa-475, University of Toronto, Department of Economics.
  2. Liao, Yuan & Simoni, Anna, 2012. "Semi-parametric Bayesian Partially Identified Models based on Support Function," MPRA Paper 43262, University Library of Munich, Germany.

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