Dependence‐robust inference using resampled statistics
开发了适用于弱依赖数据的鲁棒推断方法，通过重抽样构造检验统计量，无需知道数据的具体相关结构，适用于网络依赖等复杂依赖情形。
Summary 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.