Time Series Analysis by State Space Methods
Published on 21. June 2001
Book
Hardback
272 pages
978-0-19-852354-3 (ISBN)
Description
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.
Reviews / Votes
... provides an up-to-date exposition and comprehensive treatment of state space models in time series analysis. Journal of the Royal Statistical Society This book will be helpful to graduate students and applied statisticians working in the area of econometric modelling as well as researchers in the areas of engineering, medicine and biology where state space models are used. Journal of the Royal Statistical Society ... a good mixture of theory and practical applications ... graduate and research students will definitely enjoy this book. Also practitioners will find the book quite useful. I would also recommend it for library purchase. Journal of the Royal Statistical SocietyMore details
Series
Language
English
Place of publication
Oxford
United Kingdom
Publishing group
Oxford University Press
Target group
Professional and scholarly
Illustrations
numerous line figures
Dimensions
Height: 242 mm
Width: 160 mm
Thickness: 19 mm
Weight
539 gr
ISBN-13
978-0-19-852354-3 (9780198523543)
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Schweitzer Classification
Other editions
New editions
James Durbin | Siem Jan Koopman
Time Series Analysis by State Space Methods
Book
05/2012
2nd Edition
Oxford University Press
€135.50
Shipment within 15-20 days
Content
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