Dependence-Robust Inference Using Resampled Statistics

Abstract

We develop inference procedures robust to general forms of weak dependence. These involve test statistics constructed by resampling data in a manner that does not depend on the unknown correlation structure of the data. The statistics are simple to compute and asymptotically normal under the weak requirement that the target parameter can be consistently estimated at the parametric rate. This requirement holds for regular estimators under many well-known forms of weak dependence and justifies the claim of dependence-robustness. We consider applications to settings with unknown or complicated forms of dependence, with various forms network dependence as leading examples. We develop tests for both moment equalities and inequalities.