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Elements of Multivariate Time Series Analysis

Elements of Multivariate Time Series Analysis - Springer Series in Statistics

Softcover reprint of the original 1st Edition 1993

Paperback (31 Jul 2012)

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Publisher's Synopsis

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.

Book information

ISBN: 9781468402001
Publisher: Springer
Imprint: Springer
Pub date:
Edition: Softcover reprint of the original 1st Edition 1993
Language: English
Number of pages: 263
Weight: 397g
Height: 234mm
Width: 156mm
Spine width: 14mm