An Adversarial Approach to Structural Estimation

Tetsuya Kaji, Elena Manresa, Guillaume Pouliot

    Research output: Contribution to journalArticlepeer-review


    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.

    Original languageEnglish (US)
    Pages (from-to)2041-2063
    Number of pages23
    Issue number6
    StatePublished - Nov 2023


    • Structural estimation
    • efficient estimation
    • generative adversarial networks
    • neural networks
    • simulation-based estimation

    ASJC Scopus subject areas

    • Economics and Econometrics


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