Model Specification in Multivariate Time Series
Model Specification in Multivariate Time Series
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DOI：
10.1111/j.2517-6161.1989.tb01756.x
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发表时间：
1989
期刊：
影响因子：
--
通讯作者：
G. Tiao;R. Tsay
中科院分区：
文献类型：
--
作者：
G. Tiao;R. Tsay
总结 我们提出了一种方法，为给定的多元时间序列指定适当但简约的向量自回归移动平均（ARMA）模型。通过考虑向量过程的同期线性变换，我们在向量 ARMA 框架内引入标量分量模型的概念（a）以揭示过程可能隐藏的简化结构，（b）实现参数化的简约性，以及（c）识别可交换模型。简化结构在多元时间序列的分析中特别重要，因为它们通常从观察到的数据中并不明显，但可用于深入了解所研究的过程。用于搜索标量分量模型的分析工具是矢量过程的典型相关分析，并且通过两个实际示例说明了所提出的程序。
SUMMARY We propose a method to specify an appropriate yet parsimonious vector autoregressive moving average (ARMA) model for a given multivariate time series. By considering con temporaneous linear transformations of the vector process, we introduce the concept of scalar component models within the vector ARMA framework (a) to reveal possibly hidden simplifying structures of the process, (b) to achieve parsimony in parameterization and (c) to identify the exchangeable models. The simplifyingstructures are of particular importance in the analysis of multivariate time series because they are often not obvious from the observed data but can be used to gain insights to the process under study. The analytical tool used to search for scalar component models is a canonical correlation analysis of vector processes and the proposed procedures are illustrated via two real examples.