Time Series Analysis by State Space Methods by James Durbin

Time Series Analysis by State Space Methods

byJames Durbin, Siem Jan Koopman

Hardcover | May 10, 2012

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This new edition updates Durbin and Koopman's important text on the state space approach to time series analysis. The distinguishing feature of state space time series models is that observations are regarded as made up of distinct components such as trend, seasonal, regression elements anddisturbance terms, each of which is modelled separately. The techniques that emerge from this approach are very flexible and are capable of handling a much wider range of problems than the main analytical system currently in use for time series analysis, the Box-Jenkins ARIMA system. Additions tothis second edition include the filtering of nonlinear and non-Gaussian series.Part I of the book obtains the mean and variance of the state, of a variable intended to measure the effect of an interaction and of regression coefficients, in terms of the observations.Part II extends the treatment to nonlinear and non-normal models. For these, analytical solutions are not available so methods are based on simulation.

About The Author

James Durbin was Professor of Statistics at the London School of Economics, President of the Royal Statistical Society and President of the International Statistical Institute. He was awarded the society's bronze, silver and gold medals for his contribution to statistics. He is a fellow of the British Academy. Siem Jan Koopman has been...

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Title:Time Series Analysis by State Space MethodsFormat:HardcoverDimensions:400 pages, 9.21 × 6.14 × 0 inPublished:May 10, 2012Publisher:Oxford University PressLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:019964117X

ISBN - 13:9780199641178

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Table of Contents

1. IntroductionPart I: The linear state space model2. Local level model3. Linear Gaussian state space models4. Filtering, smoothing and forecasting5. Initialisation of Filter and smoother6. Further computational aspects7. Maximum likelihood estimation of parameters8. Illustrations of the use of the linear Gaussian modelPart II: Non-Gaussian and nonlinear state space models9. Special cases of nonlinear and non-Gaussian models10. Approximate filtering and smoothing11. Importance sampling for smoothing12. Particle filtering13. Bayesian estimation of parameters14. Non-Gaussian and nonlinear illustrationsSubject Index

Editorial Reviews

Review from previous edition: "...provides an up-to-date exposition and comprehensive treatment of state space models in time series analysis...This book will be helpful to graduate students and applied statisticians working in the area of econometric modelling as well as researchers in theareas of engineering, medicine and biology where state space models are used." --Journal of the Royal Statistical Society