An Adversarial Approach to Structural Estimation
This method addresses estimation challenges in structural models for economists, offering a novel approach with theoretical guarantees, though it is incremental in applying adversarial concepts to this domain.
The authors tackled the problem of estimating structural models by proposing adversarial estimation, a simulation-based method that formulates the estimator as a minimax problem between a generator and discriminator, and showed it attains parametric efficiency under correct specification and the parametric rate under misspecification, with application to an elderly saving model revealing the bequest motive as important across wealth distributions.
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.