State Space Modeling of Time Series

1. Front Matter

   Pages I-XVII

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2. Introduction

   • Masanao Aoki

   Pages 1-2

3. The Notion of State

   • Masanao Aoki

   Pages 3-7

4. Data Generating Processes

   • Masanao Aoki

   Pages 8-20

5. State Space and ARMA Models

   • Masanao Aoki

   Pages 21-38

6. Properties of State Space Models

   • Masanao Aoki

   Pages 39-49

7. Hankel Matrix and Singular Value Decomposition

   • Masanao Aoki

   Pages 50-70

8. Innovation Models, Riccati Equations, and Multiplier Analysis

   • Masanao Aoki

   Pages 71-98

9. State Vectors and Optimality Measures

   • Masanao Aoki

   Pages 99-104

10. Estimation of System Matrices

    • Masanao Aoki

    Pages 105-164

11. Approximate Models and Error Analysis

    • Masanao Aoki

    Pages 165-186

12. Integrated Time Series

    • Masanao Aoki

    Pages 187-228

13. Numerical Examples

    • Masanao Aoki

    Pages 229-248

14. Back Matter

    Pages 249-326

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In this book, the author adopts a state space approach to time series modeling to provide a new, computer-oriented method for building models for vector-valued time series. This second edition has been completely reorganized and rewritten. Background material leading up to the two types of estimators of the state space models is collected and presented coherently in four consecutive chapters. New, fuller descriptions are given of state space models for autoregressive models commonly used in the econometric and statistical literature. Backward innovation models are newly introduced in this edition in addition to the forward innovation models, and both are used to construct instrumental variable estimators for the model matrices. Further new items in this edition include statistical properties of the two types of estimators, more details on multiplier analysis and identification of structural models using estimated models, incorporation of exogenous signals and choice of model size. A whole new chapter is devoted to modeling of integrated, nearly integrated and co-integrated time series.

• Estimator
• Instrumentalvariablen
• Likelihood
• Regression
• Regression analysis
• Signal
• Time series
• algorithm
• algorithms
• calculus
• correlation
• geometry
• model
• modeling
• optimization

• Department of Computer Science, University of California, Los Angeles, USA

  Masanao Aoki

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