We leverage the assumption that preferences are stable across contexts to partially identify and conduct inference on the parameters of a structural model of risky choice. Working with data on households' deductible choices across three lines of insurance coverage and a model that nests expected...

We use data on households' deductible choices in auto and home insurance to estimate a structural model of risky choice that incorporates "standard" risk aversion (concave utility over final wealth), loss aversion, and nonlinear probability weighting. Our estimates indicate that nonlinear...

We use data on insurance deductible choices to estimate a structural model of risky choice that incorporates "standard" risk aversion (diminishing marginal utility for wealth) and probability distortions. We find that probability distortions - characterized by substantial overweighting of small...

We survey the literature on estimating risk preferences using field data. We concentrate our attention on studies in which risk preferences are the focal object and estimating their structure is the core enterprise. We review a number of models of risk preferences — including both expected...

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...

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...