Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/241903 
Year of Publication: 
2020
Series/Report no.: 
cemmap working paper No. CWP28/20
Publisher: 
Centre for Microdata Methods and Practice (cemmap), London
Abstract: 
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.
Subjects: 
discrete choice
limited consideration
semi-nonparametric identification
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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