Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/79286
Authors: 
Chen, Le-Yu
Year of Publication: 
2007
Series/Report no.: 
cemmap working paper, Centre for Microdata Methods and Practice CWP06/07
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.
Subjects: 
structural dynamic discrete choice models , semiparametric identification, optimal stopping time models
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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