Work Page of the book

Time Series Analysis by State Space Methods

by J. Durbin and S.J. Koopman

by Siem Jan Koopman

Last revision:June 12, 2006

News

Added link to Japanese translation.
(12-June-2006)
Added corrections to the book, in pdf format.
(7-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.
(5-Oct-2001)
This workpage is created on Thursday, February 22, 2001.
(22-Feb-2001)

About the book

Introduction

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.

Readership

Researchers and students in statistics, econometrics, biometrics, environmetrics, engineering, system theory and physics.

Time series practitioners.

Financial analysts in banking and other financial institutions.

Publication date

14 June 2001.

Contents of the book

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 ascii format:

nile.dat	Volume of Nile river at Aswan 1871-1970. (Chapter 1)
seatbelt.dat	Drivers, front and rear seats killed and seriously injured in Great Britain. (Section 9.2, 9.3)
internet.dat	Number of users logged on to an internet server. (Section 9.4)
motorcycle.dat	Motorcycle accident acceleration. (Section 9.5)
sv.dat	Pound/Dollar daily exchange rates. (Section 9.6, 14.4)
van.dat	Van drivers killed and seat belt law in Great Britain. (Section 14.2)
gas.dat	Gas consumption in UK. (Section 14.3)
boat.dat	Boat race Oxford-Cambridge data from 1829-2000. (Section 14.5)

Corrections

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

More research

Download the working paper A simple and efficient simulation smoother for state space time series analysis. 2001, by J. Durbin and S. J. Koopman in PostScript (701 KB) or zipped PostScript (175 KB) format.

Order book

Time Series Analysis by State Space Methods by J. Durbin and S.J. Koopman is published as volume 24 in the Oxford Statistical Science Series by the Oxford University Press. It can be bought from OUP-UK, OUP-US, Amazon, Barnes & Noble amongst other places.

The Japanese translation of the book can be ordered from Yahoo Japan.

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