Semi-Parametric Inference in Dynamic Binary Choice Models

Andriy Norets and Xun Tang ()
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Xun Tang: Department of Economics, University of Pennsylvania

PIER Working Paper Archive from Penn Institute for Economic Research, 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.

Keywords: Dynamic discrete choice models; Markov decision processes; semi-parametric inference; identification; Bayesian estimation; MCMC (search for similar items in EconPapers)
JEL-codes: C11 C14 (search for similar items in EconPapers)
Pages: 70 pages
Date: 2013-10-07
New Economics Papers: this item is included in nep-dcm, nep-ecm and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)

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Related works:
Journal Article: Semiparametric Inference in Dynamic Binary Choice Models (2014)
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Persistent link: https://EconPapers.repec.org/RePEc:pen:papers:13-054

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