Time Series Analysis by State Space Methods

by J. Durbin and S.J. Koopman

News

• Webpage redesign. 05-Oct-2008
• Added link to Japanese translation. 12-June-2006
• Added corrections to the book in pdf format. 07-Oct-2002
• Added the table of contents from the book, links to the datasets used in the book, to a working paper on simulation smoothing, and to some on-line book vendors. 05-Oct-2001
• This workpage was created. 22-Feb-2001

About the book

This book presents a comprehensive treatment of 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 elements, 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.

This book is aimed at researchers and students in statistics, econometrics, biometrics, environmetrics, engineering, system theory and physics. Time series practitioners, professional forecasters and analysts in research and financial institutions will find a sound theoretical development, as well as many practical examples in this book.

Contents

• Part I - The linear Gaussian state space models; Preface to Part I
  1. Introduction
  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
  8. Bayesian analysis
  9. Illustrations of the use of the linear Gaussian model
• Part II - Non-Gaussian and nonlinear state space models; Preface to Part II
  1. Non-Gaussian and nonlinear state space models
  2. Importance sampling
  3. Analysis from a classical standpoint
  4. Analysis from a Bayesian standpoint
  5. Non-Gaussian and nonlinear illustrations
• References
• Author Index
• Subject Index

Data

The data used in in the book can be downloaded here in one zip-file, or individually in text format:

Corrections

A list of corrections to the book can be downloaded here in pdf format.

The paper A simple and efficient simulation smoother for state space time series analysis. 2001, by J. Durbin and S.J. Koopman describes a more efficient simulation smoothing algorithm than the methods in the book. The working paper can be downloaded here in PDF format.

Publication and ordering information

Time Series Analysis by State Space Methods by J. Durbin and S.J. Koopman was published on June 14, 2001, as Volume 24 in the Oxford Statistical Science Series by the Oxford University Press. It can be ordered from OUP-UK, Amazon, Barnes & Noble amongst other places.

The book has been translated into Japanese, and can be ordered from Yahoo Japan.