This excellent text provides a comprehensive treatment of the state space approach to time series analysis. The distinguishing feature of state space time series models is that observations are regarded as made up of distinct components such as trend, seasonal, regression elements and disturbence terms, each of which is modelled separately. The techniques that emerge from this approach are very flexible and are capable of handling a much wider range of problems than the main analytical system currently in use for time series analysis, the Box-Jenkins ARIMA system. The book provides an excellent source for the development of practical courses on time series analysis.
Table of Contents
PART I - THE LINEAR GAUSSIAN STATE SPACE MODELS; PREFACE TO PART I ; 1. Introduction ; 2. Local level model ; 3. Linear Gaussian state space models ; 4. Filtering, smoothing and forecasting ; 5. Initialisation of filter and smoother ; 6. Further computational aspects ; 7. Maximum likelihood estimation ; 8. Bayesian analysis ; 9. Illustrations of the use of the linear Gaussian model ; PART II - NON-GAUSSIAN AND NONLINEAR STATE SPACE MODELS; PREFACE TO PART II ; 10. Non-Gaussian and nonlinear state space models ; 11. Importance sampling ; 12. Analysis from a classical standpoint ; 13. Analysis from a Bayesian standpoint ; 14. Non-Gaussian and nonlinear illustrations ; References ; Author Index ; Subject Index