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Preference Regression

Caradonna, Peter P. (2021) Preference Regression. . (Unpublished) https://resolver.caltech.edu/CaltechAUTHORS:20221027-210117499

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Abstract

This paper investigates the problem of testing and calibrating models of individual decision making. We consider a consumption space equipped with an endogenous notion of abstract ‘numeraire,’ and characterize those preferences for which the quantity of numeraire needed to compensate an agent between a pair of alternatives provides a consistent, cardinal measure of the intensity of preference. This framework includes many well-known preferences over classical commodity spaces, finite or infinite horizon consumption streams, and a wide range of models of preference over uncertainty and risk as special cases. For data consisting of observed or experimentally elicited compensation differences, we develop a least squares theory for quantifying a model’s predictive accuracy and estimating underlying parameters. We additionally provide a general class of explicit, non-parametric statistical tests of rationalizability by particular models for stochastic data. Applications to model selection, welfare analysis and elicitation of subjective beliefs are given.


Item Type:Report or Paper (Working Paper)
Related URLs:
URLURL TypeDescription
https://www.petercaradonna.com/files/regression.pdfAuthorWorking Paper
Additional Information:I am indebted to my advisor Christopher Chambers for his encouragement and innumerable helpful discussions over the course of this project. I would also like to thank Axel Anderson, Thomas Demuynck, Ivana Komunjer, Yusufcan Masatlioglu, Alexandre Poirier, Margit Reischer, Mauricio Ribeiro, Christopher Turansick, as well as seminar audiences at Georgetown, SAET and D-TEA for numerous helpful comments.
Record Number:CaltechAUTHORS:20221027-210117499
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20221027-210117499
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:117625
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:28 Oct 2022 15:00
Last Modified:28 Oct 2022 15:00

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