AI 中文总结
针对相依数据中长记忆与非平稳性难区分的问题，提出基于不同时期周期图评估的检验程序，其极限分布易处理，数值研究显示该方法优于现有方法。
AI 中文摘要
区分长记忆行为与非平稳性十分困难，因为二者的样本自协方差函数衰减都非常缓慢。现有平稳性检验要么未包含长记忆情况，要么经验水平表现较差，尤其在长记忆与非平稳的边界附近。我们提出一种检验程序，基于不同时期的周期图进行评估。本文确定的极限分布易于处理，为加权独立χ²随机变量之和。此外，数值研究表明，该方法似乎优于现有方法。
英文摘要
Distinguishing long memory behaviour from nonstationarity can be very difficult as in both cases the sample autocovariance function decays very slowly. Available stationarity tests either do not include long memory or fare poorly in terms of empirical size, especially near the boundary between long memory and nonstationarity. We propose a testing procedure based on evaluating periodograms at different epochs. Limiting distributions established here are easily tractable as sum of weighted independent $χ^2$ random variables. Moreover, numerical studies are provided to show that the proposed approach seems to outperform existing methods.
