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Identification of structural dynamic discrete choice models

  • Le-Yu Chen


    (Institute for Fiscal Studies and Academia Sinica)

This paper presents new identification 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 identification method does not rely on the availability of terminal period data and hence can be applied to infinite horizon structural dynamic models. For identification we assume availability of a continuous observed state variable that satisfies certain exclusion restrictions. If such excluded variable is accessible, we show that the structural dynamic discrete choice model is semiparametrically identified using the control function approach. This is a substantial revision of "Semiparametric identification of structural dynamic optimal stopping time models", CWP06/07.

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Paper provided by Centre for Microdata Methods and Practice, Institute for Fiscal Studies in its series CeMMAP working papers with number CWP08/09.

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Date of creation: May 2009
Date of revision:
Handle: RePEc:ifs:cemmap:08/09
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