Artikel

Nonparametric identification of dynamic decision processes with discrete and continuous choices

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.

Language: Englisch

Bibliographic citation: Journal: Quantitative Economics ; ISSN: 1759-7331 ; Volume: 5 ; Year: 2014 ; Issue: 3 ; Pages: 531-554 ; New Haven, CT: The Econometric Society

Classification: Wirtschaft

Subject: Nonparametric identification
Markov decision processes
dynamic decision processes
discrete choice
continuous choice

Event: Geistige Schöpfung

(who): Blevins, Jason R.

Event: Veröffentlichung

(who): The Econometric Society

(where): New Haven, CT

(when): 2014

DOI: doi:10.3982/QE117

Handle: http://hdl.handle.net/10419/150373

Last update: 10.03.2025, 11:43 AM CET

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Artikel

Associated

Blevins, Jason R.
The Econometric Society

Time of origin

2014

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Nonparametric identification of dynamic decision processes with discrete and continuous choices

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Other Objects (12)

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