Inference under partial identification with minimax test statistics

Author:

Isaac Loh

Keyword:

Economics, Econometrics, Econometrics (econ.EM)

journal:

date:

2024-01-23 00:00:00

Abstract

We provide a means of computing and estimating the asymptotic distributions of test statistics based on an outer minimization of an inner maximization. Such test statistics, which arise frequently in moment models, are of special interest in providing hypothesis tests under partial identification. Under general conditions, we provide an asymptotic characterization of such test statistics using the minimax theorem, and a means of computing critical values using the bootstrap. Making some light regularity assumptions, our results provide a basis for several asymptotic approximations that have been provided for partially identified hypothesis tests, and extend them by mitigating their dependence on local linear approximations of the parameter space. These asymptotic results are generally simple to state and straightforward to compute (e.g. adversarially).

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