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Detection of the Industrial Business Cycle using SETAR Models

  • Laurent Ferrara

    ()

  • Dominique Guégan

    ()

In this paper, we consider a threshold time series model in order to take into account certain stylized facts of the business cycle, such as asymmetries in the phases of the cycle. Our aim is to point out some thresholds under (over) which a signal of turning point could be given in real-time. First, we introduce the various threshold models and we discuss both their statistical theoretical and empirical properties. Especially, we review the classical techniques to estimate the number of regimes, the threshold, the delay and the parameters of the model. Then we apply these models to the Euro-zone industrial production index to detect in real-time, trough a dynamic simulation approach, the dates of peaks and throughs in the business cycle.

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File URL: http://dx.doi.org/10.1787/jbcma-v2005-art9-en
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Article provided by OECD Publishing,Centre for International Research on Economic Tendency Surveys in its journal Journal of Business Cycle Measurement and Analysis.

Volume (Year): 2005 (2005)
Issue (Month): 3 ()
Pages: 353-371

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Handle: RePEc:oec:stdkaa:5l9k5d020423
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  1. Terasvirta, T & Anderson, H M, 1992. "Characterizing Nonlinearities in Business Cycles Using Smooth Transition Autoregressive Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 7(S), pages S119-36, Suppl. De.
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