Time Series Analysis by State Space Methods

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The late James Durbin (Formerly Professor of Statistics, London School of Economics and Political Sciences) Siem Jan Koopman (Department of Econometrics, Free University, Amsterdam)

Time Series Analysis by State Space Methods

The late James Durbin (Formerly Professor of Statistics, London School of Economics and Political Sciences), Siem Jan Koopman (Department of Econometrics, Free University, Amsterdam)

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English

Oxford University Press
22 May 2012

Economic statistics; Probability & statistics; Mathematical modelling; Maths for scientists

Series: Oxford Statistical Science Series

This new edition updates Durbin & 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 and disturbance 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 to this 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.

By: The late James Durbin (Formerly Professor of Statistics London School of Economics and Political Sciences), Siem Jan Koopman (Department of Econometrics, Free University, Amsterdam)
Imprint: Oxford University Press
Country of Publication: United Kingdom
Edition: 2nd Revised edition
Volume: 38
Dimensions: Height: 235mm, Width: 161mm, Spine: 26mm
Weight: 1g
ISBN: 9780199641178
ISBN 10: 019964117X
Series: Oxford Statistical Science Series
Pages: 368
Publication Date: 22 May 2012
Audience: College/higher education , Professional and scholarly , Further / Higher Education , Undergraduate
Format: Hardback
Publisher's Status: Active

1: Introduction Part I: The linear state space model 2: Local level model 3: Linear Gaussian state space models 4: Filtering, smoothing and forecasting 5: Initialisation of Filter and smoother 6: Further computational aspects 7: Maximum likelihood estimation of parameters 8: Illustrations of the use of the linear Gaussian model Part II: Non-Gaussian and nonlinear state space models 9: Special cases of nonlinear and non-Gaussian models 10: Approximate filtering and smoothing 11: Importance sampling for smoothing 12: Particle filtering 13: Bayesian estimation of parameters 14: Non-Gaussian and nonlinear illustrations Subject Index

The late 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 was a fellow of the British Academy. Siem Jan Koopman has been Professor of Econometrics at the Free University in Amsterdam and research fellow at the Tinbergen Institute since 1999. He fullfills editorial duties at the Journal of Applied Econometrics, the Journal of Forecasting, the Journal of Multivariate Analysis and Statistica Sinica.

Reviews for Time Series Analysis by State Space Methods

`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 the areas of engineering, medicine and biology where state space models are used.' Journal of the Royal Statistical Society