State Space and Unobserved Component Models: Theory and Applications

doi:10.1017/CBO9780511617010

State Space and Unobserved Component Models

Theory and Applications

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Edited by Andrew Harvey, University of Cambridge, Siem Jan Koopman, Vrije Universiteit, Amsterdam, Neil Shephard, University of Oxford

Publisher:: Cambridge University Press
Online publication date:: January 2010
Print publication year:: 2004
Online ISBN:: 9780511617010
DOI:: https://doi.org/10.1017/CBO9780511617010

Subjects:: Economics, Econometrics and Mathematical Methods, Statistics for Econometrics, Finance and Insurance, Statistics and Probability

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This 2004 volume offers a broad overview of 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 sections: 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 literature of this discipline.

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.'

Source: Journal of the Royal Statistical Society

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State Space and Unobserved Component Models

Theory and Applications

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Contents

Frontmatter
pp i-iv

Contents
pp v-vi

Preface
pp vii-xiii

Acknowledgements
pp xiv-xiv

Part I - State space models
pp 1-2

1 - Introduction to state space time series analysis
pp 3-25

2 - State structure, decision making and related issues
pp 26-39

3 - An introduction to particle filters
pp 40-72

Part II - Testing
pp 73-74

4 - Frequency domain and wavelet-based estimation for long-memory signal plus noise models
pp 75-91

5 - A goodness-of-fit test for AR(1) models and power against state space alternatives
pp 92-101

6 - Tests for cycles
pp 102-120

Part III - Bayesian inference and bootstrap
pp 121-122

7 - Efficient Bayesian parameter estimation
pp 123-151

8 - Empirical Bayesian inference in a nonparametric regression model
pp 152-170

9 - Resampling in state space models
pp 171-202

Part IV - Applications
pp 203-204

10 - Measuring and forecasting financial variability using realised variance
pp 205-235

11 - Practical filtering for stochastic volatility models
pp 236-247

12 - On RegComponent time series models and their applications
pp 248-283

13 - State space modelling in macroeconomics and finance using SsfPackin S+Finmetrics
pp 284-335

14 - Finding genes in the human genome with hidden Markov models
pp 336-350

References
pp 351-372

Author index
pp 373-376

Subject index
pp 377-380

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State Space and Unobserved Component Models

Theory and Applications

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