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.

中文翻译：

---

使用重采样统计的依赖鲁棒推理

我们开发了对一般形式的弱依赖具有鲁棒性的推理程序。该过程利用通过以不依赖于数据的未知相关结构的方式重采样构建的测试统计量。我们证明了在目标参数可以以参数率一致估计的弱要求下，统计量是渐近正态的。这适用于许多众所周知的弱依赖形式下的常规估计量，并证明了依赖稳健性的主张。我们考虑应用到具有未知或复杂依赖形式的设置，以各种形式的网络依赖作为主要示例。我们开发了矩等式和不等式的测试。