An adversarial approach to structural estimation

Tetsuya Kaji, Elena Manresa () and Guillaume Pouliot
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Tetsuya Kaji: Institute for Fiscal Studies
Elena Manresa: Institute for Fiscal Studies and MIT
Guillaume Pouliot: Institute for Fiscal Studies

No CWP39/20, CeMMAP working papers from Centre for Microdata Methods and Practice, 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.

Date: 2020-07-21
New Economics Papers: this item is included in nep-ore
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