A unified approach to nonlinearity, structural change and outliers
AbstractThis 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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Bibliographic InfoPaper provided by Erasmus University Rotterdam, Econometric Institute in its series Econometric Institute Report with number EI 2005-09.
Date of creation: 09 Mar 2005
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Bayesian inference; threshold models; Markov-switching models; business cycle asymmetry; state-space models;
Other versions of this item:
- Giordani, Paolo & Kohn, Robert & van Dijk, Dick, 2007. "A unified approach to nonlinearity, structural change, and outliers," Journal of Econometrics, Elsevier, vol. 137(1), pages 112-133, March.
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- 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.
- Van Dijk, Dick & Franses, Philip Hans & Lucas, Andre, 1999. "Testing for Smooth Transition Nonlinearity in the Presence of Outliers," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(2), pages 217-35, April.
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