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State Space and Unobserved Component Models

State Space and Unobserved Component Models
Theory and Applications

$129.00 (C)

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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  • Date Published: July 2004
  • availability: In stock
  • format: Hardback
  • isbn: 9780521835954

$ 129.00 (C)
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About the Authors
  • Offering a broad overview of the state-of-the-art developments in the theory and applications of state space modeling, fourteen chapters from twenty-three contributors present a unique synthesis of state space methods and unobserved component models important in a wide range of subjects. They include economics, finance, environmental science, medicine and engineering. A useful reference for all researchers and students who use state space methodology, this accessible volume makes a significant contribution to the advancement of the 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
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    Reviews & endorsements

    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

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    Product details

    • Date Published: July 2004
    • format: Hardback
    • isbn: 9780521835954
    • length: 394 pages
    • dimensions: 254 x 179 x 30 mm
    • weight: 0.935kg
    • availability: In stock
  • 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 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.

  • Editors

    Andrew Harvey, University of Cambridge

    Siem Jan Koopman, Vrije Universiteit, Amsterdam

    Neil Shephard, University of Oxford

    Contributors

    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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