Identication of Structural Dynamic Discrete Choice Models

This paper presents new identi…cation results for the class of structural dynamic discrete choice models that are built upon the framework of the structural discrete Markov decision processes proposed by Rust (1994). We demonstrate how to semiparametrically identify the deep structural parameters of interest in the case where utility function of one choice in the model is parametric but the distribution of unobserved heterogeneities is nonparametric. The proposed identi…cation method does not rely on the availability of terminal period data and hence can be applied to in…nite horizon structural dynamic models. For identi…cation we assume availability of a continuous observed state variable that satis…es certain exclusion restrictions. If such excluded variable is accessible, we show that the structural dynamic discrete choice model is semiparametrically identi…ed using the control function approach.