Skip to main content
Discussion Paper

Approximating Choice Data by Discrete Choice Models

Published: May 2022
Researchers analyze random-coefficient discrete choice models and their ability to approximate nonparametric random utility models.

Examining random-coefficient discrete choice models such as the mixed logit models, researchers aim to find necessary and sufficient conditions for approximating any nonparametric random utility models across choice sets. They provide recommendations for such conditions and also provide algorithms with which to measure error in scenarios where those conditions cannot be met.

Abstract and Citation