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An Adversarial Approach to Structural Estimation

Author

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  • Tetsuya Kaji
  • Elena Manresa
  • Guillaume Pouliot

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.

Suggested Citation

  • Tetsuya Kaji & Elena Manresa & Guillaume Pouliot, 2023. "An Adversarial Approach to Structural Estimation," Econometrica, Econometric Society, vol. 91(6), pages 2041-2063, November.
  • Handle: RePEc:wly:emetrp:v:91:y:2023:i:6:p:2041-2063
    DOI: 10.3982/ECTA18707
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    References listed on IDEAS

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    1. McFadden, Daniel, 1989. "A Method of Simulated Moments for Estimation of Discrete Response Models without Numerical Integration," Econometrica, Econometric Society, vol. 57(5), pages 995-1026, September.
    2. Tetsuya Kaji & Elena Manresa & Guillaume Pouliot, 2020. "An Adversarial Approach to Structural Estimation," Papers 2007.06169, arXiv.org, revised Oct 2022.
    3. Klein, Roger W & Spady, Richard H, 1993. "An Efficient Semiparametric Estimator for Binary Response Models," Econometrica, Econometric Society, vol. 61(2), pages 387-421, March.
    4. Imbens, Guido W, 2002. "Generalized Method of Moments and Empirical Likelihood," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(4), pages 493-506, October.
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    Cited by:

    1. Sendhil Mullainathan & Ashesh Rambachan, 2024. "From Predictive Algorithms to Automatic Generation of Anomalies," NBER Working Papers 32422, National Bureau of Economic Research, Inc.
    2. Jesús Fernández-Villaverde & Galo Nuño & Jesse Perla, 2024. "Taming the Curse of Dimensionality: Quantitative Economics with Deep Learning," NBER Working Papers 33117, National Bureau of Economic Research, Inc.
    3. Jean-Jacques Forneron & Zhongjun Qu, 2024. "Fitting Dynamically Misspecified Models: An Optimal Transportation Approach," Papers 2412.20204, arXiv.org.
    4. Irene Botosaru & Isaac Loh & Chris Muris, 2024. "An Adversarial Approach to Identification," Papers 2411.04239, arXiv.org, revised Dec 2024.
    5. Harold D. Chiang, 2025. "Maximal Inequalities for Separately Exchangeable Empirical Processes," Papers 2502.11432, arXiv.org, revised Mar 2025.
    6. Yanhao & Wei & Zhenling Jiang, 2025. "Estimating Parameters of Structural Models Using Neural Networks," Papers 2502.04945, arXiv.org.

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