Semiparametric Inference in Dynamic Binary Choice Models

57 Pages Posted: 25 Apr 2012

See all articles by Andriy Norets

Andriy Norets

Princeton University - Department of Economics

Xun Tang

University of Pennsylvania - Department of Economics; Rice University - Department of Economics

Date Written: April 25, 2012

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.

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

JEL Classification: C14, C15, C25

Suggested Citation

Norets, Andriy and Tang, Xun, Semiparametric Inference in Dynamic Binary Choice Models (April 25, 2012). PIER Working Paper No. 12-017, Economic Theory Center Working Paper No. 40-2012, Available at SSRN: https://ssrn.com/abstract=2046145 or http://dx.doi.org/10.2139/ssrn.2046145

Andriy Norets

Princeton University - Department of Economics ( email )

Princeton, NJ 08544-1021
United States

Xun Tang (Contact Author)

University of Pennsylvania - Department of Economics ( email )

Ronald O. Perelman Center for Political Science
133 South 36th Street
Philadelphia, PA 19104-6297
United States
215-898-7409 (Phone)
215-573-2057 (Fax)

Rice University - Department of Economics ( email )

6100 South Main Street
Houston, TX 77005
United States

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