Elements of Multivariate Time Series Analysis
By: by Gregory C. Reinsel
Series: Springer Series in Statistics; Springer series in statistics
Publisher: Springer US
Published: 1993
Place of publication: New York, NY
Language: eng
Format: [electronic resource] /
Number of pages: 263
ISBN-13: 9781468402001; 9781468401981
Subjects: Engineering; Statistics; Chemistry; Mathematics; Economics; Physiology
Description: This book is concerned with 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, and includes a wide variety of examples drawn from many fields of application. The book presupposes a familiarity with univariate time series as might be gained from one semester 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. In addition, it presents some 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 filtering techniques.
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