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Semiparametric identification of structural dynamic optimal stopping time models

Chen, L.-Y.; (2007) Semiparametric identification of structural dynamic optimal stopping time models. (cemmap Working Papers CWP06/). Institute for Fiscal Studies: London, UK. Green open access

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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.

Type: Working / discussion paper
Title: Semiparametric identification of structural dynamic optimal stopping time models
Open access status: An open access version is available from UCL Discovery
Publisher version: http://www.cemmap.ac.uk/publications.php
Language: English
Keywords: Structural dynamic discrete choice models, semiparametric identification, optimal stopping time models
UCL classification: UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of SandHS > Dept of Economics
URI: https://discovery.ucl.ac.uk/id/eprint/14674
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