  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.
