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.

Look Inside

• Table of Contents (78 KB)
• Copyright Information Page (120 KB)
• Front Matter (258 KB)
• Marketing Excerpt (230 KB)
• Index (158 KB)