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A unified approach to nonlinearity, structural change and outliers

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Author Info

  • Giordani, P.
  • Kohn, R.
  • Dijk, D.J.C. van

Abstract

This paper demonstrates that the class of conditionally linear and Gaussianstate-space models offers a general and convenient framework for simultaneouslyhandling nonlinearity, structural change and outliers in time series. Manypopular nonlinear time series models, including threshold, smooth transitionand Markov-Switching models, can be written in state-space form. It is thenstraightforward to add components that capture parameter instability andintervention effects. We advocate a Bayesian approach to estimation andinference, using an efficient implementation of Markov Chain Monte Carlosampling schemes for such linear dynamic mixture models. The general modellingframework and the Bayesian methodology are illustrated by means of severalexamples. An application to quarterly industrial production growth rates forthe G7 countries demonstrates the empirical usefulness of the approach.

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File URL: http://hdl.handle.net/1765/1910
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Bibliographic Info

Paper provided by Erasmus University Rotterdam, Econometric Institute in its series Econometric Institute Report with number EI 2005-09.

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Date of creation: 09 Mar 2005
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Handle: RePEc:dgr:eureir:1765001910

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Web page: http://www.few.eur.nl/few

Related research

Keywords: Bayesian inference; threshold models; Markov-switching models; business cycle asymmetry; state-space models;

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  1. Dijk, D.J.C. van & Franses, Ph.H.B.F. & Lucas, A., 1996. "Testing for Smooth Transition Nonlinearity in the Presence of Outliers," Econometric Institute Report EI 9622-/A, Erasmus University Rotterdam, Econometric Institute.
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