Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/241922 
Year of Publication: 
2020
Series/Report no.: 
cemmap working paper No. CWP47/20
Publisher: 
Centre for Microdata Methods and Practice (cemmap), London
Abstract: 
We propose a robust method of discrete choice analysis when agents' choice sets are unobserved. Our core model assumes nothing about agents' choice sets apart from their minimum size. Importantly, it leaves unrestricted the dependence, conditional on observables, between agents' choice sets and their preferences. We first characterize the sharp identification region of the model's parameters by a finite set of conditional moment inequalities. We then apply our theoretical findings to learn about households' risk preferences and choice sets from data on their deductible choices in auto collision insurance. We find that the data can be explained by expected utility theory with low levels of risk aversion and heterogeneous choice sets, and that more than three in four households require limited choice sets to explain their deductible choices. We also find that the data are consistent with some models of choice set formation, but not others.
Subjects: 
choice sets
discrete choice
partial identification
random utility
risk preferences
unobserved heterogeneity
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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