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Recovering Preferences from Finite Data

Chambers, Christopher P. and Echenique, Federico and Lambert, Nicolas S. (2019) Recovering Preferences from Finite Data. . (Unpublished) https://resolver.caltech.edu/CaltechAUTHORS:20210303-151215927

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Abstract

We study preferences estimated from finite choice experiments and provide sufficient conditions for convergence to a unique underlying "true" preference. Our conditions are weak, and therefore valid in a wide range of economic environments. We develop applications to expected utility theory, choice over consumption bundles, menu choice and intertemporal consumption. Our framework unifies the revealed preference tradition with models that allow for errors.


Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription
http://arxiv.org/abs/1909.05457arXivDiscussion Paper
ORCID:
AuthorORCID
Chambers, Christopher P.0000-0001-8253-0328
Echenique, Federico0000-0002-1567-6770
Additional Information:An early version circulated under the title “Preference Identification.” We are particularly grateful to Jeroen Swinkels, the coeditor and six anonymous referees for insightful comments and suggestions. We are thankful to Pathikrit Basu for finding a mistake in a prior draft of the paper and for valuable suggestions. We thank seminar participants at many institutions, the audiences of conferences and workshops at HEC Paris, Paris School of Economics, UC Berkeley, University of Pennsylvania, University of Warwick, University of York, Oxford University, and Virginia Tech. Echenique thanks the National Science Foundation for financial support (Grants SES-1558757 and CNS-518941), and the Simons Institute at UC Berkeley for its hospitality. Lambert thanks Microsoft Research and the Cowles Foundation at Yale University for their hospitality and financial support.
Funders:
Funding AgencyGrant Number
NSFSES-1558757
NSFCNS-518941
Simons InstituteUNSPECIFIED
Microsoft ResearchUNSPECIFIED
Cowles FoundationUNSPECIFIED
Record Number:CaltechAUTHORS:20210303-151215927
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20210303-151215927
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:108296
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:03 Mar 2021 23:32
Last Modified:03 Mar 2021 23:32

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