State Space and Unobserved Component Models: Theory and Applications

ISBN: 9781107407435 出版年：2004 页码：396 Andrew Harvey Siem Jan Koopman Neil Shephard Cambridge University Press

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.