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Real-time detection of the business cycle using SETAR models

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  • Laurent Ferrara

    (CES - Centre d'économie de la Sorbonne - CNRS : UMR8174 - Université Paris I - Panthéon-Sorbonne)

  • Dominique Guegan

    ()
    (CES - Centre d'économie de la Sorbonne - CNRS : UMR8174 - Université Paris I - Panthéon-Sorbonne)

Abstract

We consider a threshold time series model in order to take into account some stylized facts of the business cycle such as asymmetries in the phases. 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. Specifically, 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 area industrial production index to detect, through a dynamic simulation approach, the dates of peaks and thoughs in business cycle.

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Bibliographic Info

Paper provided by HAL in its series Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) with number halshs-00185372.

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Date of creation: 2006
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Publication status: Published, Growth and Cycle in the Euro-zone, Palgrave MacMillan, New York (Ed.), 2006, 221-232
Handle: RePEc:hal:cesptp:halshs-00185372

Note: View the original document on HAL open archive server: http://halshs.archives-ouvertes.fr/halshs-00185372
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Related research

Keywords: Economic cycle – Turning point detection Threshold model – Euro area IPI;

References

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  1. Clements, M.P. & Smith J., 1998. "Evaluating The Forecast of Densities of Linear and Non-Linear Models: Applications to Output Growth and Unemployment," The Warwick Economics Research Paper Series (TWERPS) 509, University of Warwick, Department of Economics.
  2. Dick van Dijk & Timo Terasvirta & Philip Hans Franses, 2002. "Smooth Transition Autoregressive Models — A Survey Of Recent Developments," Econometric Reviews, Taylor & Francis Journals, Taylor & Francis Journals, vol. 21(1), pages 1-47.
  3. Marcelle Chauvet & Jeremy Piger, 2002. "Identifying business cycle turning points in real time," Working Paper, Federal Reserve Bank of Atlanta 2002-27, Federal Reserve Bank of Atlanta.
  4. Michael Artis, 2003. "Is there a European Business Cycle?," CESifo Working Paper Series 1053, CESifo Group Munich.
  5. Hansen Bruce E., 1997. "Inference in TAR Models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 2(1), pages 1-16, April.
  6. Potter, Simon M, 1995. "A Nonlinear Approach to US GNP," Journal of Applied Econometrics, John Wiley & Sons, Ltd., John Wiley & Sons, Ltd., vol. 10(2), pages 109-25, April-Jun.
  7. Hans-Martin Krolzig & Michael Clements, 2000. "Business Cycle Asymmetries: Characterisation and Testing based on Markov-Switching Autoregressions," Economics Series Working Papers 2000-W32, University of Oxford, Department of Economics.
  8. Krolzig, H.-M. & Toro, J., 2001. "Classical And Modern Business Cycle Measurement: The European Case," Economics Series Working Papers 9960, University of Oxford, Department of Economics.
  9. Terasvirta, T & Anderson, H M, 1992. "Characterizing Nonlinearities in Business Cycles Using Smooth Transition Autoregressive Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., John Wiley & Sons, Ltd., vol. 7(S), pages S119-36, Suppl. De.
  10. Clements, Michael P & Smith, Jeremy, 1999. "A Monte Carlo Study of the Forecasting Performance of Empirical SETAR Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., John Wiley & Sons, Ltd., vol. 14(2), pages 123-41, March-Apr.
  11. Hans-Martin Krolzig, 2001. "Markov-Switching Procedures for Dating the Euro-Zone Business Cycle," Vierteljahrshefte zur Wirtschaftsforschung / Quarterly Journal of Economic Research, DIW Berlin, German Institute for Economic Research, vol. 70(3), pages 339-351.
  12. Harding, Don & Pagan, Adrian, 2003. "A comparison of two business cycle dating methods," Journal of Economic Dynamics and Control, Elsevier, Elsevier, vol. 27(9), pages 1681-1690, July.
  13. Proietti Tommaso, 1998. "Characterizing Asymmetries in Business Cycles Using Smooth-Transition Structural Time-Series Models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 3(3), pages 1-18, October.
  14. Wai-Cheung Ip, 2003. "Testing and estimation of thresholds based on wavelets in heteroscedastic threshold autoregressive models," Biometrika, Biometrika Trust, Biometrika Trust, vol. 90(3), pages 703-716, September.
  15. Ferrara, Laurent, 2003. "A three-regime real-time indicator for the US economy," Economics Letters, Elsevier, vol. 81(3), pages 373-378, December.
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