Elements of Multivariate Time Series Analysis

✍ Scribed by Gregory C. Reinsel (auth.)

• Publisher: Springer US
• Year: 1993
• Tongue: English
• Leaves: 277
• Series: Springer Series in Statistics
• Category: Library

✦ 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.

✦ Table of Contents

Front Matter....Pages i-xiv
Vector Time Series and Model Representations....Pages 1-20
Vector ARMA Time Series Models and Forecasting....Pages 21-51
Canonical Structure of Vector ARMA Models....Pages 52-73
Initial Model Building and Least Squares Estimation for Vector AR Models....Pages 74-110
Maximum Likelihood Estimation and Model Checking for Vector ARMA Models....Pages 111-153
Reduced-Rank and Nonstationary Co-Integrated Models....Pages 154-191
State-Space Models, Kalman Filtering, and Related Topics....Pages 192-225
Back Matter....Pages 226-264

✦ Subjects

Statistics, general; Economic Theory; Mathematical and Computational Biology; Math. Applications in Chemistry; Computational Intelligence; Physiological, Cellular and Medical Topics