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Sequential Methodology for Signaling Business Cycle Turning Points

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  • Vasyl Golosnoy
  • Jens Hogrefe

Abstract

The dates of U.S. business cycle are reported by NBER with a considerable delay, so an early notion of turning points is of particular interest. This paper proposes a novel sequential approach designed for timely signaling these turning points. A directional cumulated sum decision rule is adapted for the purpose of on-line monitoring of transitions between subsequent phases of economic activity. The introduced procedure shows a sound detection ability for business cycle peaks and troughs compared to the established dynamic factor Markov switching methodology. It exhibits a range of theoretical optimality properties for early signaling, moreover, it is transparent and easy to implement

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File URL: https://www.ifw-members.ifw-kiel.de/publications/sequential-methodology-for-signaling-business-cycle-turning-points-1/sequential-methodology-for-signaling-business-cycle-turning-points.pdf
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Bibliographic Info

Paper provided by Kiel Institute for the World Economy in its series Kiel Working Papers with number 1528.

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Length: 26 pages
Date of creation: Jun 2009
Date of revision:
Handle: RePEc:kie:kieliw:1528

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Keywords: Business cycle; CUSUM control chart; Dynamic Factor Markov switching models; Early signaling; NBER dating;

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  1. Kim, C-J., 1991. "Dynamic Linear Models with Markov-Switching," Papers, York (Canada) - Department of Economics 91-8, York (Canada) - Department of Economics.
  2. Harding, Don & Pagan, Adrian, 2006. "Synchronization of cycles," Journal of Econometrics, Elsevier, Elsevier, vol. 132(1), pages 59-79, May.
  3. Marcelle Chauvet & Jeremy M. Piger, 2005. "A comparison of the real-time performance of business cycle dating methods," Working Papers, Federal Reserve Bank of St. Louis 2005-021, Federal Reserve Bank of St. Louis.
  4. Edward E. Leamer, 2008. "What's a Recession, Anyway?," NBER Working Papers 14221, National Bureau of Economic Research, Inc.
  5. David Bock & Eva Andersson & Marianne Frisén, 2005. "Statistical surveillance of cyclical processes with application to turns in business cycles," Journal of Forecasting, John Wiley & Sons, Ltd., John Wiley & Sons, Ltd., vol. 24(7), pages 465-490.
  6. Layton, Allan P., 1996. "Dating and predicting phase changes in the U.S. business cycle," International Journal of Forecasting, Elsevier, Elsevier, vol. 12(3), pages 417-428, September.
  7. James H. Stock & Mark W. Watson, 2003. "Has the Business Cycle Changed and Why?," NBER Chapters, National Bureau of Economic Research, Inc, in: NBER Macroeconomics Annual 2002, Volume 17, pages 159-230 National Bureau of Economic Research, Inc.
  8. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, Econometric Society, vol. 57(2), pages 357-84, March.
  9. Chauvet, Marcelle, 1998. "An Econometric Characterization of Business Cycle Dynamics with Factor Structure and Regime Switching," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 969-96, November.
  10. Gerhard Bry & Charlotte Boschan, 1971. "Cyclical Analysis of Time Series: Selected Procedures and Computer Programs," NBER Books, National Bureau of Economic Research, Inc, National Bureau of Economic Research, Inc, number bry_71-1.
  11. David N. DeJong & Roman Liesenfeld & Jean-François Richard, 2005. "A Nonlinear Forecasting Model of GDP Growth," The Review of Economics and Statistics, MIT Press, vol. 87(4), pages 697-708, November.
  12. Marianne Frisén, 2003. "Statistical Surveillance. Optimality and Methods," International Statistical Review, International Statistical Institute, International Statistical Institute, vol. 71(2), pages 403-434, 08.
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