Semiparametric Inference in Dynamic Binary Choice Models

Andriy Norets and X. Tang

The Review of Economic Studies, 2014, vol. 81, issue 3, 1229-1262

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 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.

Date: 2014
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Working Paper: Semi-Parametric Inference in Dynamic Binary Choice Models (2013)
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