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Detection of the industrial business cycle using SETAR models

  • Ferrara, Laurent
  • Guégan, Dominique

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.

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File URL: http://mpra.ub.uni-muenchen.de/4389/1/MPRA_paper_4389.pdf
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Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 4389.

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Date of creation: Sep 2005
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Publication status: Published in Journal of Business Cycle Measurement and Analysis 3.2(2005): pp. 353-372
Handle: RePEc:pra:mprapa:4389
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  1. Arturo Estrella & Frederic S. Mishkin, 1996. "Predicting U.S. recessions: financial variables as leading indicators," Research Paper 9609, Federal Reserve Bank of New York.
  2. Coakley, Jerry & Fuertes, Ana-Maria & Perez, Maria-Teresa, 2003. "Numerical issues in threshold autoregressive modeling of time series," Journal of Economic Dynamics and Control, Elsevier, vol. 27(11-12), pages 2219-2242, September.
  3. Potter, Simon M, 1999. " Nonlinear Time Series Modelling: An Introduction," Journal of Economic Surveys, Wiley Blackwell, vol. 13(5), pages 505-28, December.
  4. Kajal Lahiri & Wenxiong Yao & Peg Young, 2003. "Cycles in the Transportation Sector and the Aggregate Economy," Discussion Papers 03-14, University at Albany, SUNY, Department of Economics.
  5. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-84, March.
  6. Jacques Anas & Laurent Ferrara, 2004. "Detecting Cyclical Turning Points: The ABCD Approach and Two Probabilistic Indicators," Journal of Business Cycle Measurement and Analysis, OECD Publishing,Centre for International Research on Economic Tendency Surveys, vol. 2004(2), pages 193-225.
  7. Clements, Michael P. & Smith, Jeremy, 2001. "Evaluating forecasts from SETAR models of exchange rates," Journal of International Money and Finance, Elsevier, vol. 20(1), pages 133-148, February.
  8. Michael Artis, 2003. "Is there a European Business Cycle?," CESifo Working Paper Series 1053, CESifo Group Munich.
  9. Hans-Martin Krolzig & Juan Toro, 2001. "Classical and Modern Business Cycle Measurement: The European Case," Economics Series Working Papers 60, University of Oxford, Department of Economics.
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  13. Gilles Dufrénot & Dominique Guegan & Anne Peguin-Feissolle, 2005. "Long-memory dynamics in a SETAR model - Applications to stock markets," Post-Print halshs-00179339, HAL.
  14. Agustín Maravall & Cristophe Planas, 1996. "Estimation Error and the Specification of Unobserved Component Models," Banco de Espa�a Working Papers 9608, Banco de Espa�a.
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  16. Artis, M. & Krolzig, H.-M. & Toro, J., 1999. "The European Business Cycle," Economics Working Papers eco99/24, European University Institute.
  17. Bruce E. Hansen, 1996. "Sample Splitting and Threshold Estimation," Boston College Working Papers in Economics 319., Boston College Department of Economics, revised 12 May 1998.
  18. 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.
  19. Marcelle Chauvet & Jeremy M. Piger, 2003. "Identifying business cycle turning points in real time," Review, Federal Reserve Bank of St. Louis, issue Mar, pages 47-61.
  20. 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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  24. Dufrenot, Gilles & Guegan, Dominique & Peguin-Feissolle, Anne, 2005. "Modelling squared returns using a SETAR model with long-memory dynamics," Economics Letters, Elsevier, vol. 86(2), pages 237-243, February.
  25. G. Pfann & P. Schotman & R. Tschernig, 1994. "Nonlinear Interest Rate Dynamics and Implications for the Term Structure," SFB 373 Discussion Papers 1994,43, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
  26. 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.
  27. Harding, Don & Pagan, Adrian, 2003. "A comparison of two business cycle dating methods," Journal of Economic Dynamics and Control, Elsevier, vol. 27(9), pages 1681-1690, July.
  28. 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.
  29. Gonzalo, Jesus & Pitarakis, Jean-Yves, 2002. "Estimation and model selection based inference in single and multiple threshold models," Journal of Econometrics, Elsevier, vol. 110(2), pages 319-352, October.
  30. 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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