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https://dx.doi.org/10.7907/0vm...
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Axiomatizations of the Mixed Logit Model

descriptionPublicationkeyboard_double_arrow_right Research , Report 15 Sep 2017 United States Publisher:California Institute of Technology
Authors: Saito, Kota;

doi: 10.7907/0vm8e-qys12

Axiomatizations of the Mixed Logit Model

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.

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.

Accepted Version - sswp1433.pdf

Country
United States
Related Organizations
Keywords

mixed logit, 330, Random utility, random choice, mixed logit, random coefficients, random choice, random coefficients, Random utility

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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