An Adversarial Approach to Structural Estimation
60 Pages Posted: 12 Oct 2020
Date Written: July 20, 2020
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: Suggested Citation