Published September 15, 2017
| Version Accepted Version
Working Paper
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Axiomatizations of the Mixed Logit Model
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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.
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.Attached Files
Accepted Version - sswp1433.pdf
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Additional details
Identifiers
- Eprint ID
- 99361
- Resolver ID
- CaltechAUTHORS:20191018-102413328
Funding
- NSF
- SES-1558757
Dates
- Created
-
2019-10-18Created from EPrint's datestamp field
- Updated
-
2019-10-18Created from EPrint's last_modified field
Caltech Custom Metadata
- Caltech groups
- Social Science Working Papers
- Series Name
- Social Science Working Paper
- Series Volume or Issue Number
- 1433