Working Paper

Discrete choice under risk with limited consideration

Authors

Matthew Thirkettle, Francesca Molinari, Levon Barseghyan

Published Date

24 June 2020

Type

Working Paper (CWP28/20)

This paper is concerned with learning decision makers’ preferences using data on observed choices from a finite set of risky alternatives. We propose a discrete choice model with unobserved heterogeneity in consideration sets and in standard risk aversion. We obtain sufficient conditions for the model’s semi-nonparametric point identification, including in cases where consideration depends on preferences and on some of the exogenous variables. Our method yields an estimator that is easy to compute and is applicable in markets with large choice sets. We illustrate its properties using a dataset on property insurance purchases.


Previous version

Discrete choice under risk with limited consideration
Matthew Thirkettle, Francesca Molinari, Levon Barseghyan
CWP08/19