Econometrica: Nov, 2023, Volume 91, Issue 6
An Adversarial Approach to Structural Estimation
https://doi.org/10.3982/ECTA18707
p. 2041-2063
Tetsuya Kaji, Elena Manresa, Guillaume Pouliot
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.
Supplemental Material
Supplement to "An Adversarial Approach to Structural Estimation"
Tetsuya Kaji, Elena Manresa, and Guillaume Pouliot
This online appendix contains material not found within the manuscript.
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Supplement to "An Adversarial Approach to Structural Estimation"
Tetsuya Kaji, Elena Manresa, and Guillaume Pouliot
The replication package for this paper is available at https://doi.org/10.5281/zenodo.8310266. The Journal checked the data and codes included in the package for their ability to reproduce the results in the paper and approved online appendices. Given the highly demanding nature of the algorithms, the reproducibility checks were run on a simplified version of the code, which is also available in the replication package.
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