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 micr...

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Bibliographic Details
Published in:	Quantitative economics Vol. 5; no. 3; pp. 531 - 554
Main Author:	Blevins, Jason R
Format:	Journal Article
Language:	English
Published:	New Haven, CT The Econometric Society 01-11-2014 Blackwell Publishing Ltd John Wiley & Sons, Inc
Subjects:	C14 C23 C25 C51 continuous choice discrete choice dynamic decision processes Econometrics Economic models Economic theory Markov decision processes Microeconomics Nonparametric identification Nonparametric statistics Optimization Power
Online Access:	Get full text
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Summary:	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.
ISSN:	1759-7331 1759-7323 1759-7331
DOI:	10.3982/QE117