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.

翻译：我们制定了对一般依赖性薄弱形式的可靠推论程序。这些程序采用不取决于数据未知相关结构的重新抽样方式构建的测试性统计数据。我们证明,在目标参数能够以参数速率一致估算这一要求薄弱的情况下,这些统计数据几乎是正常的。这适用于许多众所周知依赖性薄弱形式的定期估算者,并证明依赖性-强压性的说法是有道理的。我们考虑对不明或复杂依赖性形式的环境的应用,以各种形式的网络依赖性为主要例子。我们开发了对时平和和不平等的测试。