State Space and Unobserved Component Models
Theory and Applications
Published on 10. June 2004
Book
Hardback
394 pages
978-0-521-83595-4 (ISBN)
Description
This 2004 volume offers a broad overview of developments in the theory and applications of state space modeling. With fourteen chapters from twenty-three contributors, it offers a unique synthesis of state space methods and unobserved component models that are important in a wide range of subjects, including economics, finance, environmental science, medicine and engineering. The book is divided into four sections: introductory papers, testing, Bayesian inference and the bootstrap, and applications. It will give those unfamiliar with state space models a flavour of the work being carried out as well as providing experts with valuable state of the art summaries of different topics. Offering a useful reference for all, this accessible volume makes a significant contribution to the literature of this discipline.
Reviews / Votes
Review of the hardback: 'There is much in this book, and I would heartily recommend it to specialists and librarians. I know of no other comparable text.' Journal of the Royal Statistical SocietyMore details
Language
English
Place of publication
Cambridge
United Kingdom
Target group
Professional and scholarly
Dimensions
Height: 254 mm
Width: 179 mm
Thickness: 30 mm
Weight
935 gr
ISBN-13
978-0-521-83595-4 (9780521835954)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Andrew Harvey | Siem Jan Koopman | Neil Shephard
State Space and Unobserved Component Models
Theory and Applications
Book
09/2012
Cambridge University Press
€71.60
Shipment within 15-20 days
Persons
Andrew Harvey is Professor of Econometrics and Fellow of Corpus Christi College, University of Cambridge. He is the author of the Econometric Analysis of Time Series (1981), Time Series Models (1981) and Forecasting: Structural Time Series Models and the Kalman Filter (1989). Siem Jan Koopman is Professor of Econometrics at the Free University Amsterdam and Research Fellow of Tinbergen Institute, Amsterdam. He has published in international journals and is co-author of Time Series Analysis by State Space Models (with J. Durbin, 2001). Neil Shephard is Professor of Economics and Official Fellow, Nuffield College, Oxford University. He is the Editor of Econometrics Journal.
Content
Part I. State Space Models: 1. Introduction to state space time series analysis James Durbin; 2. State structure, decision making and related issues Peter Whittle; 3. An introduction to particle filters Simon Maskell; Part II. Testing: 4. Frequence domain and wavelet-based estimation for long-memory signal plus noise models Katsuto Tanaka; 5. A goodness-of-fit test for AR (1) models and power against state-space alternatives T. W. Anderson and Michael A. Stephens; 6. Test for cycles Andrew C. Harvey; Part III. Bayesian Inference and Bootstrap: 7. Efficient Bayesian parameter estimation Sylvia Fruehwirth-Schnatter; 8. Empirical Bayesian inference in a nonparametric regression model Gary Koop and Dale Poirier; 9. Resampling in state space models David S. Stoffer and Kent D. Wall; Part IV. Applications: 10. Measuring and forecasting financial variability using realised variance Ole E. Barndorff-Nielsen, Bent Nielsen, Neil Shephard and Carla Ysusi; 11. Practical filtering for stochastic volatility models Jonathan R. Stroud, Nicholas G. Polson and Peter Mueller; 12. On RegComponent time series models and their applications William R. Bell; 13. State space modeling in macroeconomics and finance using SsfPack in S+Finmetrics Eric Zivot, Jeffrey Wang and Siem Jan Koopman; 14. Finding genes in the human genome with hidden Markov models Richard Durbin.