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Harvey / Koopman / Shephard

State Space and Unobserved Component Models

Theory and Applications

Medium: Buch
ISBN: 978-0-521-83595-4
Verlag: Cambridge-Hitachi
Erscheinungstermin: 12.07.2004
Lieferfrist: bis zu 10 Tage

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.


Produkteigenschaften


  • Artikelnummer: 9780521835954
  • Medium: Buch
  • ISBN: 978-0-521-83595-4
  • Verlag: Cambridge-Hitachi
  • Erscheinungstermin: 12.07.2004
  • Sprache(n): Englisch
  • Auflage: Erscheinungsjahr 2004
  • Produktform: Gebunden
  • Gewicht: 934 g
  • Seiten: 394
  • Format (B x H x T): 175 x 256 x 29 mm
  • Ausgabetyp: Kein, Unbekannt
Autoren/Hrsg.

Herausgeber

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.