State Space and Unobserved Component Models
Theory and Applications
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Product details:
- Publisher Cambridge University Press
- Date of Publication 10 June 2004
- ISBN 9780521835954
- Binding Hardback
- No. of pages394 pages
- Size 254x179x30 mm
- Weight 935 g
- Language English 0
Categories
Short description:
A comprehensive overview of developments in the theory and application of state space modeling, first published in 2004.
MoreLong 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.
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 Society
Table of Contents:
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 Fr&&&252;hwirth-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 M&&&252;ller; 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.
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