Semiparametric Inference in Dynamic Binary Choice Models
57 Pages Posted: 25 Apr 2012
Date Written: April 25, 2012
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
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