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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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PRINTED FROM OXFORD SCHOLARSHIP ONLINE (www.oxfordscholarship.com). (c) Copyright Oxford University Press, 2019. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in OSO for personal use. date: 12 November 2019

Parametric nonlinear models

Parametric nonlinear models

Chapter:
(p.28) 3 Parametric nonlinear 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/9780199587148.003.0003

In this chapter, a number of most commonly applied nonlinear time series models are being considered. As opposed to the previous chapter, these models do not generally have their origin in economic theory. Many of the models nest a linear model are therefore relatively easily interpretable. The models include regression models such as the smooth transition, switching regression and Markov switching models. They also include models based on rather general functional forms such as artificial neural network models and polynomial models. More rarely applied models such as bilinear or max‐min models are also mentioned. Models with stochastic coefficients also receive attention. Areas of application of these models to economic time series are briefly mentioned.

Keywords:   artificial neural network, bilinear model, hidden Markov model, Kolmogorov‐Gabor polynomial, neural network, nonlinear autoregressive model, smooth transition regression, switching regression, threshold autoregression, time‐varying parameter model

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