State Space and Unobserved Component Models: Theory and Applications

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Overview

Offering a broad overview of the state-of-the-art developments in the theory and applications of state space modeling, fourteen chapters from twenty-three contributors present a unique synthesis of state space methods and unobserved component models important in a wide range of subjects. They include economics, finance, environmental science, medicine and engineering. A useful reference for all researchers and students who use state space methodology, this accessible volume makes a significant contribution to the advancement of the discipline.

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Editorial Reviews

From the Publisher
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.' Journal of the Royal Statistical Society
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Product Details

  • ISBN-13: 9780521835954
  • Publisher: Cambridge University Press
  • Publication date: 6/28/2004
  • Pages: 394
  • Product dimensions: 6.85 (w) x 9.72 (h) x 1.18 (d)

Meet the Author

Andrew Harvey is Professor of Econometrics and Fellow of Corpus Christi College, University of Cambridge. He is the author of the Econometric Analysis of Time Series (1981), Time Series Models (1981) and Forecasting: Structural Time Series Models and the Kalman Filter (1989).

Siem Jan Koopman is Professor of Econometrics at the Free University Amsterdam and Research Fellow of Tinbergen Institute, Amsterdam. He has published in international journals and is co-author of Time Series Analysis by State Space Models (with J. Durbin, 2001).

Neil Shephard is Professor of Economics and Official Fellow, Nuffield College, Oxford University. He is the Editor of Econometrics Journal.

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Table of Contents

Pt. I State space models 1
1 Introduction to state space time series analysis 3
2 State structure, decision making and related issues 26
3 An introduction to particle filters 40
Pt. II Testing 73
4 Frequency domain and wavelet-based estimation for long-memory signal plus noise models 75
5 A goodness-of-fit test for AR(1) models and power against state space alternatives 92
6 Tests for cycles 102
Pt. III Bayesian inference and bootstrap 121
7 Efficient Bayesian parameter estimation 123
8 Empirical Bayesian inference in a nonparametric regression model 152
9 Resampling in state space models 171
Pt. IV Applications 203
10 Measuring and forecasting financial variability using realised variance 205
11 Practical filtering for stochastic volatility models 236
12 On RegComponent time series models and their applications 248
13 State spaces modelling in macroeconomics and finance using SsfPack in S+Finmetrics 284
14 Finding genes in the human genome with hidden Markov models 336
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