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Semiparametric Inference in Dynamic Binary Choice Models, Second Version

  • Andriy Norets

    ()

    (Department of Economics, Princeton University)

  • Xun Tang

    ()

    (Department of Economics, University of Pennsylvania)

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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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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