Time Series Analysis by State Space Methods

• Clear, comprehensive introudction to the state space approach to time series analysis
• Written by leaders in the field
• Complete treatment of linear Gaussian models
• New material including the filtering of nonlinear and non-Gaussian series and exercise sections

New to this edition

• Extensive foundation of filtering and smoothing
• Updated discussions on simulation smoothing methods
• New sections on dynamic factor analysis, state smoothing analysis and more detail on Markov chain Monte Carlo methods
• Analysis of nonlinear and non-Gaussian state space methods
• Now includes exercise sections

Readership: Researchers in statistics, econometrics, biometrics, environmetrics, engineering, system theory and physics. Financial analysts in banking and other financial institutions.

The late James Durbin, Formerly London School of Economics and University College London, and Siem Jan Koopman, Department of Econometrics, Vrije Universiteit Amsterdam

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.

Review(s) 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

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