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State Space and Unobserved Component Models
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  • Page extent: 394 pages
  • Size: 247 x 174 mm
  • Weight: 0.935 kg

Library of Congress

  • Dewey number: 003
  • Dewey version: 22
  • LC Classification: QA402 .S835 2004
  • LC Subject headings:
    • State-space methods--Congresses
    • System analysis--Congresses

Library of Congress Record

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 (ISBN-13: 9780521835954 | ISBN-10: 052183595X)

DOI: 10.2277/052183595X

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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.

• Cutting-edge scholarship that makes a significant contribution to the field of state space modelling and applications of those models • All chapters are written by authors with an established track record in this area • A reference for researchers across many disparate fields who all use state space methodology


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 Frühwirth-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 Müller; 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.


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


James Durbin, Peter Whittle, Simon Maskell, Katsuto Tanaka, T. W. Anderson, Michael A. Stephens, Andrew C. Harvey, Sylvia Frühwirth-Schnatter, Gary Koop, Dale Poirier, David S. Stoffer, Kent D. Wall, Ole E. Barndorff-Nielsen, Bent Nielsen, Neil Shephard, Carla Ysusi, Jonathan R. Stroud, Nicholas G. Polson, Peter Müller, William R. Bell, Eric Zivot, Jeffrey Wang, Siem Jan Koopman, Richard Durbin

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