Bootstrapping time series requires dealing with the dependence that may exist within the sample. Several strategies have been proposed, but their validity has only been proven for short memory series and there has been little progress in their theoretical properties under long memory, where strong persistence may invalidate conventional techniques. The first contribution is to review all these recent advances, paying particular attention to those approaches that do not rely on parametric models and offering a guide for practitioners who wish to use them in semiparametric or nonparametric contexts. The second contribution is a Monte Carlo analysis of the applicability of these bootstrap techniques for approximating the distribution of low frequency estimators of the memory parameter based on spectral behaviour at frequencies close to the origin.

自举时间序列需要处理样本中可能存在的依赖性。已经提出了几种策略，但它们的有效性仅在短记忆序列中得到证明，并且在长记忆下其理论特性几乎没有进展，在长记忆中，强持久性可能会使传统技术失效。第一个贡献是回顾所有这些最新进展，特别关注那些不依赖参数模型的方法，并为希望在半参数或非参数环境中使用它们的从业者提供指南。第二个贡献是对这些自举技术的适用性进行蒙特卡洛分析，用于根据接近原点的频率处的频谱行为来近似存储参数的低频估计器的分布。