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Time Series Analysis by State Space MethodsSecond Edition$
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James Durbin and Siem Jan Koopman

Print publication date: 2012

Print ISBN-13: 9780199641178

Published to Oxford Scholarship Online: December 2013

DOI: 10.1093/acprof:oso/9780199641178.001.0001

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Linear state space models

Linear state space models

Chapter:
(p.43) 3 Linear state space models
Source:
Time Series Analysis by State Space Methods
Author(s):

J. Durbin

S.J. Koopman

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780199641178.003.0003

This chapter shows how structural time series models can be put into state space form. It puts Box-Jenkins ARIMA models into state space form, thus demonstrating that these models are special cases of state space models. It discusses the history of exponential smoothing and shows how it relates to simple forms of state space and ARIMA models. It considers various aspects of regression with or without time-varying coefficients or autocorrelated errors. It also presents a treatment of dynamic factor analysis. Further topics discussed include simultaneous modelling series from different sources, benchmarking, continuous time models, and spline smoothing in discrete and continuous time.

Keywords:   state space analysis, time series analysis, spline smoothing analysis

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