Time Series Analysis by State Space Methods
Second Edition
Series: Oxford Statistical Science Series; 38;
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Product details:
- Edition number 2
- Publisher OUP Oxford
- Date of Publication 3 May 2012
- ISBN 9780199641178
- Binding Hardback
- No. of pages368 pages
- Size 235x161x25 mm
- Weight 680 g
- Language English
- Illustrations 34 b/w illustrations 0
Categories
Short description:
This new edition updates Durbin & Koopman's important text on the state space approach to time series analysis providing a more comprehensive treatment, including the filtering of nonlinear and non-Gaussian series. The book provides an excellent source for the development of practical courses on time series analysis.
MoreLong description:
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.
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.
Table of Contents:
Introduction
Part I: The linear state space model
Local level model
Linear Gaussian state space models
Filtering, smoothing and forecasting
Initialisation of Filter and smoother
Further computational aspects
Maximum likelihood estimation of parameters
Illustrations of the use of the linear Gaussian model
Part II: Non-Gaussian and nonlinear state space models
Special cases of nonlinear and non-Gaussian models
Approximate filtering and smoothing
Importance sampling for smoothing
Particle filtering
Bayesian estimation of parameters
Non-Gaussian and nonlinear illustrations
Subject Index
