State space and unobserved component models : theory and applications

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書誌事項

State space and unobserved component models : theory and applications

edited by Andrew Harvey, Siem Jan Koopman, Neil Shephard

Cambridge University Press, 2004

大学図書館所蔵 件 / 全17件

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注記

Includes bibliographical references (p. 351-372) and indexes

内容説明・目次

内容説明

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.

目次

  • 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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