Table of contents

Part I: Introduction

1. Basic concepts
2. Getting started (sample chapter available)

Part II: Essentials

3. Linear innovations state space models
4. Non-linear and heteroscedastic innovations state space models
5. Estimation of innovations state space models
6. Prediction distributions and intervals
7. Selection of models

Part III: Further topics

8. Normalizing seasonal components
9. Models with regressor variables
10. Some properties of linear models
11. Reduced forms and relationships with ARIMA models
12. Linear innovations state space models with random seed states
13. Conventional state space models
14. Time series with multiple seasonal patterns (with Phillip Gould)
15. Non-linear models for positive data (with Muhammad Akram)
16. Models for count data
17. Vector exponential smoothing (with Ashton de Silva)

Part IV: Applications

18. Inventory control application
19. Conditional heteroscedasticity and applications in finance
20. Economic applications: the Beveridge-Nelson decomposition (with Chin Nam Low and
    Heather Anderson)