Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/150373 
Year of Publication: 
2014
Citation: 
[Journal:] Quantitative Economics [ISSN:] 1759-7331 [Volume:] 5 [Issue:] 3 [Publisher:] The Econometric Society [Place:] New Haven, CT [Year:] 2014 [Pages:] 531-554
Publisher: 
The Econometric Society, New Haven, CT
Abstract: 
This paper establishes conditions for nonparametric identification of dynamic optimization models in which agents make both discrete and continuous choices. We consider identification of both the payoff function and the distribution of unobservables. Models of this kind are prevalent in applied microeconomics and many of the required conditions are standard assumptions currently used in empirical work. We focus on conditions on the model that can be implied by economic theory and assumptions about the data generating process that are likely to be satisfied in a typical application. Our analysis is intended to highlight the identifying power of each assumption individually, where possible, and our proofs are constructive in nature.
Subjects: 
Nonparametric identification
Markov decision processes
dynamic decision processes
discrete choice
continuous choice
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc Logo
Document Type: 
Article

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