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Semiparametric identification of structural dynamic optimal stopping time models

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  • Le-Yu Chen

    (Institute for Fiscal Studies and Academia Sinica)

Abstract

This paper presents new identification results for the class of structural dynamic optimal stopping time 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 the utility function of an absorbing choice in the model is parametric but the distribution of unobserved heterogeneity is nonparametric. Our identification strategy depends on availability of a continuous observed state variable that satisfies certain exclusion restrictions. If such excluded variable is accessible, we show that the dynamic optimal stopping model is semiparametrically identified using control function approaches.

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File URL: http://cemmap.ifs.org.uk/wps/cwp0706.pdf
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Bibliographic Info

Paper provided by Centre for Microdata Methods and Practice, Institute for Fiscal Studies in its series CeMMAP working papers with number CWP06/07.

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Date of creation: Mar 2007
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Handle: RePEc:ifs:cemmap:06/07

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Keywords: Structural dynamic discrete choice models; semiparametric identification; optimal stopping;

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