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Modelling Nonlinear Economic Time Series$
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Timo Teräsvirta, Dag Tjøstheim, and Clive W. J. Granger

Print publication date: 2010

Print ISBN-13: 9780199587148

Published to Oxford Scholarship Online: May 2011

DOI: 10.1093/acprof:oso/9780199587148.001.0001

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Time‐varying parameters and state space models

Time‐varying parameters and state space models

Chapter:
(p.219) 9 Time‐varying parameters and state space models
Source:
Modelling Nonlinear Economic Time Series
Author(s):

Timo Teräsvirta

Dag Tjøstheim (Contributor Webpage)

W. J. Granger

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780199587148.003.0009

Linear state space models have become popular in time series, and there are applications to many fields. The Kalman filter is often a fundamental tool. In this chapter it is shown that there are extensions of these concepts to a nonlinear framework through such devices as the extended Kalman filter and particle filters. Hidden Markov chains represents an alternative but related technique, where parameters are replaced by stochastic processes; i.e., Markov chains. The chapter also contains a short section on estimating these types of models.

Keywords:   state space, Kalman filter, particle filter, hidden Markov chains

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