Adversarial Estimators. (arXiv:2204.10495v3 [econ.EM] UPDATED)

Web: http://arxiv.org/abs/2204.10495

We develop an asymptotic theory of adversarial estimators ('A-estimators').
They generalize maximum-likelihood-type estimators ('M-estimators') as their
average objective is maximized by some parameters and minimized by others. This
class subsumes the continuous-updating Generalized Method of Moments,
Generative Adversarial Networks and more recent proposals in machine learning
and econometrics. In these examples, researchers state which aspects of the
problem may in principle be used for estimation, and an adversary learns how to
emphasize them optimally. We derive the convergence rates of …

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