论文标题
本地部分自相关功能和某些应用
The Local Partial Autocorrelation Function and Some Applications
论文作者
论文摘要
经典的常规和部分自相关功能是用于固定时间序列建模和分析的强大工具。但是，越来越多地认识到，许多时间序列不是静止的，并且使用经典的全球自相关可以给出误导性的答案。本文介绍了局部部分自相关函数的两个估计量，并确定其渐近特性。然后，本文说明了这些新估计器在模拟和实时序列上的使用。这些示例清楚地证明了当地估计器对表现非组织性的时间序列的强大实际好处。
The classical regular and partial autocorrelation functions are powerful tools for stationary time series modelling and analysis. However, it is increasingly recognized that many time series are not stationary and the use of classical global autocorrelations can give misleading answers. This article introduces two estimators of the local partial autocorrelation function and establishes their asymptotic properties. The article then illustrates the use of these new estimators on both simulated and real time series. The examples clearly demonstrate the strong practical benefits of local estimators for time series that exhibit nonstationarities.
