MONITORING PROCEDURES TO DETECT UNIT ROOTS AND STATIONARITY
检测单位根与平稳性的序贯监测程序AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要明确研究I(0)与I(1)序列的序贯监测及行为改变的变点模型，涉及时间序列和结构变化。
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检测单位根与平稳性的序贯监测程序
- 研究问题
- 在时间序列逐步到达时，如何序贯判断其具有I(0)还是I(1)性质，并检测观测过程中平稳性行为发生改变的情形？
- 方法与思路
- 作者以核加权方差比统计量的序贯版本构造停止时间，推导多类过程、局部备择、近单位根与变点模型的极限分布。
- 材料陈述的贡献
- 摘要称提供了序贯监测的初步理论结果及两类变点模型的极限规律；模拟显示程序有较高功效且常能较早作出判断。
- 阅读关注点
- 应核对停止规则、核与带宽选择、监测起点及临界值，并检查两类变点模型下的误报控制和检测延迟表现。
- 阅读边界
- 仅依据所给出版社摘要；具体调参方式、误报率和检测时间数值未说明，不能将模拟中的较早判断视为普遍保证。
原文摘要
When analyzing time series an important issue is to decide whether the time series is stationary or a random walk. Relaxing these notions, we consider the problem to decide in favor of the I(0) or I(1) property. Fixed-sample statistical tests for that problem are well studied in the literature. In this paper we provide first results for the problem of monitoring sequentially a time series. Our stopping times are based on a sequential version of a kernel-weighted variance-ratio statistic. The asymptotic distributions are established for I(1) processes, a rich class of stationary processes, possibly affected by local nonparametric alternatives, and the local-to-unity model. Further, we consider the two interesting change-point models where the time series changes its behavior after a certain fraction of the observations and derive the associated limiting laws. Our Monte Carlo studies show that the proposed detection procedures have high power when interpreted as a hypothesis test and that the decision can often be made very early.The financial support of the DFG (Deutsche Forschungsgemeinschaft, SFB 475, Reduction of Complexity in Multivariate Data Structures) is gratefully acknowledged. I thank two anonymous referees for constructive and helpful remarks that improved the paper and Dipl.-Math. Sabine Teller for proofreading a revised version.