Elements of Multivariate Time Series Analysis
Elements of Multivariate Time Series Analysis
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DOI：
10.2307/2965460
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发表时间：
1995-08
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影响因子：
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通讯作者：
G. Reinsel
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文献类型：
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作者：
G. Reinsel
本研究致力于多元时间序列数据的分析。这些数据可能出现在商业和经济、工程、地球物理科学、农业和许多其他领域。重点是提供对分析这类数据有用的基本概念和方法的说明。这本书的前提是熟悉单变量时间序列，可能从一个学期的研究生课程中获得，但它是独立的。它涵盖了基本的主题，如平稳过程的自协方差矩阵，向量ARMA模型及其性质，预测ARMA过程，向量AR和ARMA模型的最小二乘和最大似然估计技术，以及模型构建的相关似然比测试程序。此外，还介绍了更高级的主题和技术，包括降秩结构、结构指标、标量分量模型、向量时间序列的典型相关分析、多元非平稳单位根模型和协整结构、状态空间模型和卡尔曼滤波技术。
This study is devoted to the analysis of multivariate time series data. Such data might arise in business and economics, engineering, geophysical sciences, agriculture, and many other fields. The emphasis is on providing an account of the basic concepts and methods which are useful in analyzing such data. The book presupposes a familiarity with univariate time series as might be gained from one term of a graduate course, but it is otherwise self-contained. It covers the basic topics such as autocovariance matrices of stationary processes, vector ARMA models and their properties, forecasting ARMA processes, least squares and maximum likelihood estimation techniques for vector AR and ARMA models, and associated likelihood ratio testing procedures for model building. In addition, it presents more advanced topics and techniques including reduced rank structure, structural indices, scalar component models, canonical correlation analyses for vector time series, multivariate nonstationary unit root models and co-integration structure, and state-space models and Kalman flltering techniques.