State Space and Unobserved Component Models: Theory and Applications

by Edited by Andrew Harvey , Siem Jan Koopman , Neil Shephard

• ISBN13: 9780521835954
• ISBN10: 052183595X
• Format: Hardcover
• Copyright: 2004-07-12
• Publisher: Cambridge University Press

Summary

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.

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 Frü

hwirth-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 Mü

ller

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.