State Space and Unobserved Component Models: Theory and Applications

doi:10.1017/CBO9780511617010

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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Contents

Frontmatter
pp i-iv
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Contents
pp v-vi
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Preface
pp vii-xiii
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Acknowledgements
pp xiv-xiv
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Part I - State space models
pp 1-2
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1 - Introduction to state space time series analysis
pp 3-25
- By James Durbin, Department of Statistics, London School of Economics and Political Science
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2 - State structure, decision making and related issues
pp 26-39
- By Peter Whittle, Statistical Laboratory, Centre for Mathematical Sciences, University of Cambridge
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3 - An introduction to particle filters
pp 40-72
- By Simon Maskell, Department of Engineering, University of Cambridge
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Part II - Testing
pp 73-74
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4 - Frequency domain and wavelet-based estimation for long-memory signal plus noise models
pp 75-91
- By Katsuto Tanaka, Hitotsubashi University, Tokyo
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5 - A goodness-of-fit test for AR(1) models and power against state space alternatives
pp 92-101
- By T.W. Anderson, Departments of Statistics and Economics, Stanford University, Michael A. Stephens, Department of Statistics and Actuarial Science, Simon Fraser University
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6 - Tests for cycles
pp 102-120
- By Andrew C. Harvey, Faculty of Economics and Politics, University of Cambridge
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Part III - Bayesian inference and bootstrap
pp 121-122
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7 - Efficient Bayesian parameter estimation
pp 123-151
- By Sylvia Frühwirth-Schnatter, Department of Applied Statistics (IFAS), Johannes Kepler University
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8 - Empirical Bayesian inference in a nonparametric regression model
pp 152-170
- By Gary Koop, Department of Economics, University of Leicester, Dale Poirier, Department of Economics, University of California at Irvine
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9 - Resampling in state space models
pp 171-202
- By David S. Stoffer, Department of Statistics, University of Pittsburgh, Kent D. Wall, Defense Resources Management Institute, Naval Postgraduate School
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Part IV - Applications
pp 203-204
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10 - Measuring and forecasting financial variability using realised variance
pp 205-235
- By Ole E. Barndorff-Nielsen, Department of Mathematical Sciences, University of Aarhus, Bent Nielsen, Nuffield College, University of Oxford, Neil Shephard, Nuffield College, University of Oxford, Carla Ysusi, Department of Statistics, University of Oxford
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11 - Practical filtering for stochastic volatility models
pp 236-247
- By Jonathon R. Stroud, Department of Statistics, Wharton School, University of Pennsylvania, Nicholas G. Polson, Graduate School of Business, University of Chicago, Peter Müller, M. D. Anderson Cancer Center, University of Texas
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12 - On RegComponent time series models and their applications
pp 248-283
- By William R. Bell, Statistical Research Divison, US Census Bureau
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13 - State space modelling in macroeconomics and finance using SsfPackin S+Finmetrics
pp 284-335
- By Eric Zivot, Department of Economics, University of Washington, Jeffrey Wang, Ronin Capital LLC, Siem Jan Koopman, Department of Econometrics and Operations Research, Vrije University, Amsterdam
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14 - Finding genes in the human genome with hidden Markov models
pp 336-350
- By Richard Durbin, Wellcome Trust Sanger Institute
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References
pp 351-372
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Author index
pp 373-376
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Subject index
pp 377-380
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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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