Overview
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.
Full Product Details
Author: The late James Durbin (Formerly Professor of Statistics, London School of Economics and Political Sciences) ,
Siem Jan Koopman (Department of Econometrics, Free University, Amsterdam)
Publisher: Oxford University Press
Imprint: Oxford University Press
Edition: 2nd Revised edition
Volume: 38
Dimensions:
Width: 16.10cm
, Height: 2.60cm
, Length: 23.50cm
Weight: 0.680kg
ISBN: 9780199641178
ISBN 10: 019964117
Pages: 368
Publication Date: 03 May 2012
Audience:
College/higher education
,
Professional and scholarly
,
Postgraduate, Research & Scholarly
,
Professional & Vocational
Format: Hardback
Publisher's Status: Active
Availability: To order
Stock availability from the supplier is unknown. We will order it for you and ship this item to you once it is received by us.
Reviews
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
`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
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 *
`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
Author Information
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.
