局部偏自相关函数及其应用
The Local Partial Autocorrelation Function and Some Applications
摘要：
经典的正则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，使用经典的全球自相关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。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
