Elements of Multivariate Time Series Analysis

1. Front Matter

   Pages i-xiv

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2. Vector Time Series and Model Representations

   • Gregory C. Reinsel

   Pages 1-20

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3. Vector ARMA Time Series Models and Forecasting

   • Gregory C. Reinsel

   Pages 21-51

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4. Canonical Structure of Vector ARMA Models

   • Gregory C. Reinsel

   Pages 52-73

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5. Initial Model Building and Least Squares Estimation for Vector AR Models

   • Gregory C. Reinsel

   Pages 74-110

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6. Maximum Likelihood Estimation and Model Checking for Vector ARMA Models

   • Gregory C. Reinsel

   Pages 111-153

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7. Reduced-Rank and Nonstationary Co-Integrated Models

   • Gregory C. Reinsel

   Pages 154-191

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8. State-Space Models, Kalman Filtering, and Related Topics

   • Gregory C. Reinsel

   Pages 192-225

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9. Back Matter

   Pages 226-264

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The use of methods of time series analysis in the study of multivariate time series has become of increased interest in recent years. Although the methods are rather well developed and understood for univarjate time series analysis, the situation is not so complete for the multivariate case. This book is designed to introduce the basic concepts and methods that are useful in the analysis and modeling of multivariate time series, with illustrations of these basic ideas. The development includes both traditional topics such as autocovariance and auto­ correlation matrices of stationary processes, properties of vector ARMA models, forecasting ARMA processes, least squares and maximum likelihood estimation techniques for vector 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, and state-space models and Kalman filtering techniques and applications. This book concentrates on the time-domain analysis of multivariate time series, and the important subject of spectral analysis is not considered here. For that topic, the reader is referred to the excellent books by Jenkins and Watts (1968), Hannan (1970), Priestley (1981), and others.

• Likelihood
• Radiologieinformationssystem
• correlation
• economics
• forecasting
• integration
• time series analysis

• Department of Statistics, University of Wisconsin, Madison, Madison, USA

  Gregory C. Reinsel

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