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Axiomatizations of the Mixed Logit Model

Saito, Kota (2017) Axiomatizations of the Mixed Logit Model. Social Science Working Paper, 1433. California Institute of Technology , Pasadena, CA. (Unpublished) https://resolver.caltech.edu/CaltechAUTHORS:20191018-102413328

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Abstract

A mixed logit function, also known as a random-coefficients logit function, is an integral of logit functions. The mixed logit model is one of the most widely used models in the analysis of discrete choice. Observed behavior is described by a random choice function, which associates with each choice set a probability measure over the choice set. I obtain several necessary and sufficient conditions under which a random choice function becomes a mixed logit function. One condition is easy to interpret and another condition is easy to test.


Item Type:Report or Paper (Working Paper)
ORCID:
AuthorORCID
Saito, Kota0000-0003-1189-8912
Additional Information:This paper was first presented at the University of Tokyo on July 29, 2017. I appreciate the valuable discussions I had with Kim Border, Federico Echenique, Hidehiko Ichimura, Yimeng Li, Jay Lu, and Matt Shum. Jay Lu also read the manuscript and offered helpful comments. This research is supported by Grant SES1558757 from the National Science Foundation.
Group:Social Science Working Papers
Funders:
Funding AgencyGrant Number
NSFSES-1558757
Subject Keywords:Random utility, random choice, mixed logit, random coefficients
Series Name:Social Science Working Paper
Issue or Number:1433
Record Number:CaltechAUTHORS:20191018-102413328
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20191018-102413328
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:99361
Collection:CaltechAUTHORS
Deposited By: Katherine Johnson
Deposited On:18 Oct 2019 17:27
Last Modified:18 Oct 2019 17:27

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