A unified approach to nonlinearity, structural change and outliers

Paolo Giordani, Robert Kohn () and Dick van Dijk

No EI 2005-09, Econometric Institute Research Papers from Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute

Abstract: 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.

Keywords: Bayesian inference; Markov-switching models; business cycle asymmetry; state-space models; threshold models (search for similar items in EconPapers)
JEL-codes: C11 C22 E32 (search for similar items in EconPapers)
Date: 2005-03-09
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Citations: View citations in EconPapers (4)

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Journal Article: A unified approach to nonlinearity, structural change, and outliers (2007)
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