This book is justified by the use of methods of time series analysis in the study of multivariate time series, that has become of increased interest in recent years. In spite of the fact that the methods are rather well-developed and understood for univariate time series analysis, the situation is not so good for the multivariate case. The present book is dedicated to introducing the basic concepts and methods that are useful in the analysis and modelling of multivariate time series. The book includes both traditional topics such as auto-covariance and auto correlation matrices of stationary processes, properties of vector ARMA models, forecasting of the ARMA processes, least-squares and maximum likelihood estimation techniques for AR and ARMA models, and model checking diagnostics for residuals, as well as topics of more recent interest for vector ARMA models such as reduced-rank structure, structural indices, scalar component models, canonical correlation analyses for vector time series, multivariate unit-root models and cointegration structure, state space models and Kalman filtering techniques and applications.
by Gregory C. Reinsel
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