An Adversarial Approach to Structural Estimation

• 成果类型: Article
• 署名作者: Kaji, Tetsuya; Manresa, Elena; Pouliot, Guillaume
• 署名单位: University of Chicago; New York University; University of Chicago
• 刊物名称: ECONOMETRICA
• ISSN/ISSBN: 0012-9682
• DOI: 10.3982/ECTA18707
• 发表日期: 2023
• 页码: 2041-2063
• 关键词: MOMENTS; models

摘要

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.