An Adversarial Approach to Structural Estimation

Tetsuya Kaji, Elena Manresa and Guillaume Pouliot

Papers from arXiv.org

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 simulated observations using the structural model) and a discriminator (which classifies whether an observation is simulated). 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 our estimator uncovers the bequest motive as an important source of saving across the wealth distribution, not only for the rich.

Date: 2020-07, Revised 2022-10
New Economics Papers: this item is included in nep-cmp and nep-ecm
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Published in Econometrica, 91(6), 2041-2063, November 2023

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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2007.06169

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