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An Adversarial Approach to Structural Estimation

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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. 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.

Suggested Citation

  • Tetsuya Kaji & Elena Manresa & Guillaume Pouliot, 2020. "An Adversarial Approach to Structural Estimation," Papers 2007.06169, arXiv.org, revised Oct 2022.
  • Handle: RePEc:arx:papers:2007.06169
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    References listed on IDEAS

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    1. Mariacristina De Nardi & Eric French & John B. Jones, 2010. "Why Do the Elderly Save? The Role of Medical Expenses," Journal of Political Economy, University of Chicago Press, vol. 118(1), pages 39-75, February.
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    Cited by:

    1. Michael Pollmann, 2020. "Causal Inference for Spatial Treatments," Papers 2011.00373, arXiv.org, revised Jan 2023.
    2. Hui Chen & Antoine Didisheim & Simon Scheidegger, 2021. "Deep Structural Estimation:With an Application to Option Pricing," Cahiers de Recherches Economiques du Département d'économie 21.14, Université de Lausanne, Faculté des HEC, Département d’économie.
    3. Sebastian Galiani & Juan Pantano, 2021. "Structural Models: Inception and Frontier," NBER Working Papers 28698, National Bureau of Economic Research, Inc.
    4. Tetsuya Kaji & Elena Manresa & Guillaume Pouliot, 2023. "An Adversarial Approach to Structural Estimation," Econometrica, Econometric Society, vol. 91(6), pages 2041-2063, November.

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