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Multivariate Time Series Analysis and Applications

Author William W. S. Wei
Release Date: 2019/03/01
ISBN: 9781119502852
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Book Description

An essential guide on high dimensional multivariate time series including all the latest topics from one of the leading experts in the field

Following the highly successful and much lauded book, Time Series Analysis—Univariate and Multivariate Methods, this new work by William W.S. Wei focuses on high dimensional multivariate time series, and is illustrated with numerous high dimensional empirical time series. Beginning with the fundamentalconcepts and issues of multivariate time series analysis,this book covers many topics that are not found in general multivariate time series books. Some of these are repeated measurements, space-time series modelling, and dimension reduction. The book also looks at vector time series models, multivariate time series regression models, and principle component analysis of multivariate time series. Additionally, it provides readers with information on factor analysis of multivariate time series, multivariate GARCH models, and multivariate spectral analysis of time series.

With the development of computers and the internet, we have increased potential for data exploration. In the next few years, dimension will become a more serious problem. Multivariate Time Series Analysis and its Applications provides some initial solutions, which may encourage the development of related software needed for the high dimensional multivariate time series analysis.

  • Written by bestselling author and leading expert in the field
  • Covers topics not yet explored in current multivariate books
  • Features classroom tested material
  • Written specifically for time series courses

Multivariate Time Series Analysis and its Applications is designed for an advanced time series analysis course. It is a must-have for anyone studying time series analysis and is also relevant for students in economics, biostatistics, and engineering. 

Table of Contents

  1. Cover
  2. About the author
  3. Preface
  4. About the companion website
  5. 1 Fundamental concepts and issues in multivariate time series analysis
    1. 1.1 Introduction
    2. 1.2 Fundamental concepts
    3. Projects
    4. References
  6. 2 Vector time series models
    1. 2.1 Vector moving average processes
    2. 2.2 Vector autoregressive processes
    3. 2.3 Vector autoregressive moving average processes
    4. 2.4 Nonstationary vector autoregressive moving average processes
    5. 2.5 Vector time series model building
    6. 2.6 Seasonal vector time series model
    7. 2.7 Multivariate time series outliers
    8. 2.8 Empirical examples
    9. Software code
    10. Projects
    11. References
  7. 3 Multivariate time series regression models
    1. 3.1 Introduction
    2. 3.2 Multivariate multiple time series regression models
    3. 3.3 Estimation of the multivariate multiple time series regression model
    4. 3.4 Vector time series regression models
    5. 3.5 Empirical Example III – Total mortality and air pollution in California
    6. Software code
    7. Projects
    8. References
  8. 4 Principle component analysis of multivariate time series
    1. 4.1 Introduction
    2. 4.2 Population PCA
    3. 4.3 Implications of PCA
    4. 4.4 Sample principle components
    5. 4.5 Empirical examples
    6. Software code
    7. Projects
    8. References
  9. 5 Factor analysis of multivariate time series
    1. 5.1 Introduction
    2. 5.2 The orthogonal factor model
    3. 5.3 Estimation of the factor model
    4. 5.4 Factor rotation
    5. 5.5 Factor scores
    6. 5.6 Factor models with observable factors
    7. 5.7 Another empirical example – Yearly U.S. sexually transmitted diseases (STD)
    8. 5.8 Concluding remarks
    9. Software code
    10. Projects
    11. References
  10. 6 Multivariate GARCH models
    1. 6.1 Introduction
    2. 6.2 Representations of multivariate GARCH models
    3. 6.3 O‐GARCH and GO‐GARCH models
    4. 6.4 Estimation of GO‐GARCH models
    5. 6.5 Properties of the weighted scatter estimator
    6. 6.6 Empirical examples
    7. Software code
    8. Projects
    9. References
  11. 7 Repeated measurements
    1. 7.1 Introduction
    2. 7.2 Multivariate analysis of variance
    3. 7.3 Models utilizing time series structure
    4. 7.4 Nested random effects model
    5. 7.5 Further generalization and remarks
    6. 7.6 Another empirical example – the oral condition of neck cancer patients
    7. Software code
    8. Projects
    9. References
  12. 8 Space–time series models
    1. 8.1 Introduction
    2. 8.2 Space–time autoregressive integrated moving average (STARIMA) models
    3. 8.3 Generalized space–time autoregressive integrated moving average (GSTARIMA) models
    4. 8.4 Iterative model building of STARMA and GSTARMA models
    5. 8.5 Empirical examples
    6. Software code
    7. Projects
    8. References
  13. 9 Multivariate spectral analysis of time series
    1. 9.1 Introduction
    2. 9.2 Spectral representations of multivariate time series processes
    3. 9.3 The estimation of the spectral density matrix
    4. 9.4 Empirical examples of stationary vector time series
    5. 9.5 Spectrum analysis of a nonstationary vector time series
    6. 9.6 Empirical spectrum example of nonstationary vector time series
    7. Software code
    8. Projects
    9. References
  14. 10 Dimension reduction in high‐dimensional multivariate time series analysis
    1. 10.1 Introduction
    2. 10.2 Existing methods
    3. 10.3 The proposed method for high‐dimension reduction
    4. 10.4 Simulation studies
    5. 10.5 Empirical examples
    6. 10.6 Further discussions and remarks
    7. 10.A Appendix: Parameter estimation results of various procedures
    8. Software code
    9. Projects
    10. References
  15. Author index
  16. Subject index
  17. End User License Agreement
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