Data and Code for: Discrete Choice under Risk with Limited Consideration

Principal Investigator(s): View help for Principal Investigator(s) Levon Barseghyan, Cornell University; Francesca Molinari, Cornell University; Matthew Thirkettle, Rice University

Version: View help for Version V1

Name	File Type	Size	Last Modified
Broad_ARC_Observables_B_66.mat	application/x-matlab-data	594.6 KB	02/23/2021 08:41:AM
Coll_ARC_B_66.mat	application/x-matlab-data	96.4 KB	02/23/2021 08:41:AM
Coll_ARC_observables_B_66.mat	application/x-matlab-data	109.7 KB	02/23/2021 08:41:AM
Coll_ARC_prop_shift_B_66.mat	application/x-matlab-data	65.2 KB	02/23/2021 08:41:AM
Coll_ARC_prop_shift_observables_B_66.mat	application/x-matlab-data	170.6 KB	02/23/2021 08:41:AM
RUM_Broad_Observables_B_66.mat	application/x-matlab-data	138.4 KB	02/23/2021 08:41:AM
RUM_coll_X_B_66.mat	application/x-matlab-data	138.5 KB	02/23/2021 08:41:AM

Project Description

Summary:

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.

Scope of Project

Subject Terms: discrete choice; limited consideration; semi-nonparametric identification

JEL Classification:
      C14 Semiparametric and Nonparametric Methods: General
      C25 Single Equation Models; Single Variables: Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
      C50 Econometric Modeling: General

Geographic Coverage: USA

Universe:

Data come from a U.S. insurance company

Methodology

Data Source: Data come from a U.S. insurance company

Unit(s) of Observation: Individuals

Related Publications

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Published Versions

V1 [2021-05-26]

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