Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/241914 
Erscheinungsjahr: 
2020
Schriftenreihe/Nr.: 
cemmap working paper No. CWP39/20
Verlag: 
Centre for Microdata Methods and Practice (cemmap), London
Zusammenfassung: 
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.
Schlagwörter: 
structural estimation
generative adversarial networks
neuralnetworks
simulated method of moments
indirect inference
JEL: 
C13
C45
Persistent Identifier der Erstveröffentlichung: 
Dokumentart: 
Working Paper

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