  We develop inference procedures robust to general forms of weak dependence.
The procedures utilize test statistics constructed by resampling in a manner
that does not depend on the unknown correlation structure of the data. We prove
that the statistics are asymptotically normal under the weak requirement that
the target parameter can be consistently estimated at the parametric rate. This
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 of
network dependence as leading examples. We develop tests for both moment
equalities and inequalities.
