An Adversarial Approach to Structural Estimation

TL;DR

This paper introduces an adversarial estimation method for structural models, using a minimax game between a generator and a discriminator, achieving efficient and fast convergence rates with neural networks, and applied to savings behavior analysis.

Contribution

It presents a novel simulation-based adversarial estimation approach employing neural networks, improving efficiency and convergence in structural model estimation.

Findings

Estimator attains parametric efficiency with rich discriminators.

Neural networks enable fast convergence and adaptivity.

Application reveals the bequest motive influences savings across wealth levels.

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.