An Adversarial Approach to Structural Estimation

Tetsuya Kaji (tkaji@chicagobooth.edu), Elena Manresa (elena.manresa@nyu.edu) and Guillaume Pouliot (guillaumepouliot@uchicago.edu)
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Tetsuya Kaji: University of Chicago - Booth School of Business
Elena Manresa: New York University - Department of Economics
Guillaume Pouliot: University of Chicago - Harris School of Public Policy

No 2020-144, Working Papers from Becker Friedman Institute for Research In Economics

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 (search for similar items in EconPapers)
JEL-codes: C13 C45 (search for similar items in EconPapers)
Pages: 59 pages
Date: 2020
New Economics Papers: this item is included in nep-cmp and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

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