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An adversarial approach to structural estimation

Author

Listed:
  • Tetsuya Kaji

    (Institute for Fiscal Studies)

  • Elena Manresa

    (Institute for Fiscal Studies and MIT)

  • Guillaume Pouliot

    (Institute for Fiscal Studies)

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 synthetic observations using the structural model) and a discriminator (which classifies if an observation is synthetic). 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 including gender and health profiles in the discriminator 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," CeMMAP working papers CWP39/20, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  • Handle: RePEc:ifs:cemmap:39/20
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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. 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.
    2. Ramis Khabibullin & Sergei Seleznev, 2022. "Fast Estimation of Bayesian State Space Models Using Amortized Simulation-Based Inference," Papers 2210.07154, arXiv.org.
    3. Sebastian Galiani & Juan Pantano, 2021. "Structural Models: Inception and Frontier," NBER Working Papers 28698, National Bureau of Economic Research, Inc.
    4. Michael Pollmann, 2020. "Causal Inference for Spatial Treatments," Papers 2011.00373, arXiv.org, revised Jan 2023.
    5. Jonas Metzger, 2022. "Adversarial Estimators," Papers 2204.10495, arXiv.org, revised Jun 2022.
    6. Hui Chen & Antoine Didisheim & Simon Scheidegger, 2021. "Deep Structural Estimation: With an Application to Option Pricing," Papers 2102.09209, arXiv.org.
    7. Max H. Farrell & Tengyuan Liang & Sanjog Misra, 2020. "Deep Learning for Individual Heterogeneity: An Automatic Inference Framework," Papers 2010.14694, arXiv.org, revised Jul 2021.
    8. Michael P. Leung & Pantelis Loupos, 2022. "Unconfoundedness with Network Interference," Papers 2211.07823, arXiv.org.

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