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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: 18 November 2019

Nonparametric models

Nonparametric models

Chapter:
(p.252) 10 Nonparametric 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/9780199587148.003.0010

A difficulty of nonparametric techniques is the curse of dimensionality. One cannot estimate nonparametrically in high dimensions unless restrictions are made. The present chapter considers a selection of such more restrictive models. The techniques are intermediate between purely parametric and purely nonparametric. Prime among these models are additive models and the closely related functional coefficient models. The nonparametric structure is then typically allowed to depend on just one or two variables at a time. There is also the possibility of letting a nonparametric term depend on a linear combination of variables as in the index models or in projection pursuit. Finally, there are semiparametric models, where some of the variables are modelled parametrically, some nonparametrically.

Keywords:   additive models, backfitting, functional coefficient model, index model, projection pursuit

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