The local partial autocorrelation function and some applications
The local partial autocorrelation function and some applications
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DOI：
10.1214/20-ejs1748
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发表时间：
2020-01
影响因子：
1.1
通讯作者：
Rebecca Killick;M. Knight;G. Nason;I. Eckley
中科院分区：
文献类型：
--
作者：
Rebecca Killick;M. Knight;G. Nason;I. Eckley
经典的正则自相关函数和偏自相关函数是平稳时间序列建模和分析的有力工具。然而，人们越来越认识到，许多时间序列不是平稳的，使用经典的全局自相关性可能会给出误导性的答案。本文介绍了局部偏自相关函数的两种估计，并建立了它们的渐近性质。文章然后说明了使用这些新的估计模拟和真实的时间序列。这些例子清楚地表明，当地的估计时间序列表现出非平稳性的强大的实际好处。
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.