State space and unobserved component models : theory and applications
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Bibliographic Information
State space and unobserved component models : theory and applications
Cambridge University Press, 2004
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Includes bibliographical references (p. 351-372) and indexes
Description and Table of Contents
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.
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 Fruhwirth-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 Muller
- 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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