State Space and Unobserved Component Models

This volume offers a broad overview of the state-of-the-art 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 section: 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 advancement of this discipline.

Spis treści:
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.