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Detection of the industrial business cycle using SETAR models

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  • Ferrara, Laurent
  • Guégan, Dominique

Abstract

In this paper, we consider a threshold time series model in order to take into account certain stylized facts of the industrial 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. 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, through a dynamic simulation approach, the dates of peaks and troughs in the business cycle.

Suggested Citation

  • Ferrara, Laurent & Guégan, Dominique, 2005. "Detection of the industrial business cycle using SETAR models," MPRA Paper 4389, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:4389
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    Cited by:

    1. Monica Billio & Laurent Ferrara & Dominique Guegan & Gian Luigi Mazzi, 2009. "Evaluation of Nonlinear time-series models for real-time business cycle analysis of the Euro," Documents de travail du Centre d'Economie de la Sorbonne 09053, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    2. Aastveit, Knut Are & Jore, Anne Sofie & Ravazzolo, Francesco, 2016. "Identification and real-time forecasting of Norwegian business cycles," International Journal of Forecasting, Elsevier, vol. 32(2), pages 283-292.
    3. Aastveit, Knut Are & Anundsen, André K. & Herstad, Eyo I., 2019. "Residential investment and recession predictability," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1790-1799.
    4. Roque Montero, 2012. "Does Linearity in the Dynamics of Inflation Gap and Unemployment Rate Matter?," Revista de Analisis Economico – Economic Analysis Review, Universidad Alberto Hurtado/School of Economics and Business, vol. 27(1), pages 3-26, April.
    5. Singh, Tarlok, 2014. "On the regime-switching and asymmetric dynamics of economic growth in the OECD countries," Research in Economics, Elsevier, vol. 68(2), pages 169-192.
    6. Lanouar Charfeddine & Dominique Guegan, 2008. "Is it possible to discriminate between different switching regressions models? An empirical investigation," Post-Print halshs-00368358, HAL.
    7. Aastveit, Knut Are & Anundsen, André K. & Herstad, Eyo I., 2019. "Residential investment and recession predictability," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1790-1799.

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    More about this item

    Keywords

    Economic cycle; turning point detection; Threshold model; Euro-zone IPI;
    All these keywords.

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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