Econometrics of Insurance Models with Multidimensional Types

49 Pages Posted: 22 Jun 2017 Last revised: 9 Jul 2019

See all articles by Gaurab Aryal

Gaurab Aryal

University of Virginia - Department of Economics

Isabelle Perrigne

Rice University

Quang Vuong

New York University (NYU)

Date Written: July 2019


In this paper we address the identification and estimation of insurance models where insurees have private information about their risk and risk aversion. The model includes random damage and allows for several claims, while insurees choose from a finite number of coverages. We show that the joint distribution of risk and risk aversion is nonparametrically identified despite bunching due to multidimensional types and a finite number of coverage. Our identification strategy exploits the observed number of claims as well as an exclusion restriction and a support assumption. Our results apply to any form of competition. We propose a novel and computationally friendly estimation method combining kernel regression and density estimation as well as inverse Fourier transforms.

Keywords: Insurance, Identification, Nonparametric Estimation, Multivariate Adverse Selection

JEL Classification: C10, C57, L89

Suggested Citation

Aryal, Gaurab and Perrigne, Isabelle and Vuong, Quang, Econometrics of Insurance Models with Multidimensional Types (July 2019). Available at SSRN: or

Gaurab Aryal (Contact Author)

University of Virginia - Department of Economics ( email )

P.O. Box 400182
Charlottesville, VA 22904-4182
United States

Isabelle Perrigne

Rice University ( email )

6100 South Main Street
Houston, TX 77005-1892
United States

HOME PAGE: http://

Quang Vuong

New York University (NYU) ( email )

Bobst Library, E-resource Acquisitions
20 Cooper Square 3rd Floor
New York, NY 10003-711
United States


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