An Adversarial Approach to Structural Estimation
Tetsuya Kaji, Elena Manresa, Guillaume Pouliot
Econometrica · 2023 · Volume 91 · Issue 6
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.
Keywords
Structural estimation, generative adversarial networks, neural networks, simulation-based estimation, efficient estimation
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