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Time Series Analysis by State Space Methods
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Time Series Analysis by State Space Methods: Second Edition

James Durbin and Siem Jan Koopman

Abstract

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 being 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. Part I presents a full treatment of the construction and analysis of linear Gaussian state space models. The methods are based on the Kalman filter and are appropriate for a wide range of probl ... More

Keywords: unobserved components, Kalman filter and smoother, signal extraction, forecasting, maximum likelihood, extended Kalman filter, unscented Kalman filter, simulation-based methods, Monte Carlo, importance sampling

Bibliographic Information

Print publication date: 2012 Print ISBN-13: 9780199641178
Published to Oxford Scholarship Online: December 2013 DOI:10.1093/acprof:oso/9780199641178.001.0001

Authors

Affiliations are at time of print publication.

James Durbin, author
Formerly Professor of Statistics, London School of Economics and Political Sciences

Siem Jan Koopman, author
Deparment of Econometrics, Free University, Amsterdam

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