An Adversarial Approach to Structural Estimation

60 Pages Posted: 12 Oct 2020

See all articles by Tetsuya Kaji

Tetsuya Kaji

University of Chicago Booth School of Business

Elena Manresa

Massachusetts Institute of Technology (MIT) - Sloan School of Management

Guillaume Pouliot

Harris School of Public Policy

Date Written: July 20, 2020

Abstract

We propose a new simulation-based estimation method, adversarial estimation, for structural models. The estimator is formulated as the solution to a minimax problem between a generator (which generates synthetic observations using the structural model) and a discriminator (which classifies if an observation is synthetic). The discriminator maximizes the accuracy of its classification while the generator minimizes it. We show that, with a sufficiently rich discriminator, the adversarial estimator attains parametric efficiency under correct specification and the parametric rate under misspecification. We advocate the use of a neural network as a discriminator that can exploit adaptivity properties and attain fast rates of convergence. We apply our method to the elderly’s saving decision model and show that including gender and health profiles in the discriminator uncovers the bequest motive as an important source of saving across the wealth distribution, not only for the rich.

Keywords: structural estimation, generative adversarial networks, neural networks, simulated method of moments, indirect inference, efficient estimation

JEL Classification: C13, C45

Suggested Citation

Kaji, Tetsuya and Manresa, Elena and Pouliot, Guillaume, An Adversarial Approach to Structural Estimation (July 20, 2020). University of Chicago, Becker Friedman Institute for Economics Working Paper No. 2020-144, Available at SSRN: https://ssrn.com/abstract=3706365 or http://dx.doi.org/10.2139/ssrn.3706365

Tetsuya Kaji (Contact Author)

University of Chicago Booth School of Business ( email )

Chicago, IL 60637
United States

Elena Manresa

Massachusetts Institute of Technology (MIT) - Sloan School of Management ( email )

77 Massachusetts Ave. E62-663
Cambridge, MA 02142
United States

Guillaume Pouliot

Harris School of Public Policy ( email )

1155 East 60th Street
Chicago, IL 60637
United States

HOME PAGE: http://https://sites.google.com/site/guillaumeallairepouliot/

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