State Space and Unobserved Component Models: Theory and Applications

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Juni 2004



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.


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.


Andrew Harvey is Professor of Econometrics and Fellow of Corpus Christi College, University of Cambridge. He is the author of the Econometric Analysis of Time Series (1981), Time Series Models (1981) and Forecasting: Structural Time Series Models and the Kalman Filter (1989). Siem Jan Koopman is Professor of Econometrics at the Free University Amsterdam and Research Fellow of Tinbergen Institute, Amsterdam. He has published in international journals and is co-author of Time Series Analysis by State Space Models (with J. Durbin, 2001). Neil Shephard is Professor of Economics and Official Fellow, Nuffield College, Oxford University. He is the Editor of Econometrics Journal.


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
EAN: 9780521835954
ISBN: 052183595X
Untertitel: New. Sprache: Englisch.
Erscheinungsdatum: Juni 2004
Seitenanzahl: 394 Seiten
Format: gebunden
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