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A unified approach to nonlinearity, structural change and outliers

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

This paper demonstrates that the class of conditionally linear and Gaussian state-space models offers a general and convenient framework for simultaneously handling nonlinearity, structural change and outliers in time series. Many popular nonlinear time series models, including threshold, smooth transition and Markov-Switching models, can be written in state-space form. It is then straightforward to add components that capture parameter instability and intervention effects. We advocate a Bayesian approach to estimation and inference, using an efficient implementation of Markov Chain Monte Carlo sampling schemes for such linear dynamic mixture models. The general modelling framework and the Bayesian methodology are illustrated by means of several examples. An application to quarterly industrial production growth rates for the G7 countries demonstrates the empirical usefulness of the approach.

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Paper provided by Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute in its series Econometric Institute Research Papers with number EI 2005-09.

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Date of creation: 09 Mar 2005
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Handle: RePEc:ems:eureir:1910
Contact details of provider: Postal: Postbus 1738, 3000 DR Rotterdam
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Web page: http://www.eur.nl/ese

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