Semiparametric Inference in Dynamic Binary Choice Models, Second Version
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.
|Date of creation:||14 Apr 2010|
|Date of revision:||17 Apr 2012|
|Contact details of provider:|| Postal: 3718 Locust Walk, Philadelphia, PA 19104|
Web page: http://economics.sas.upenn.edu/pier
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