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Identifying business cycle turning points with sequential Monte Carlo methods: an online and real-time application to the Euro area

  • Monica Billio
  • Roberto Casarin

We propose a new approach for detecting turning points and forecasting the level of economic activity in the business cycle. We make use of coincident indicators and of nonlinear and non-Gaussian latent variable models. We thus combine the ability of nonlinear models to capture the asymmetric features of the business cycle with information on the current state of the economy provided by coincident indicators. Our approach relies upon sequential Monte Carlo filtering techniques applied to time-nonhomogenous Markov-switching models. The transition probabilities are driven by a beta-distributed stochastic component and by a set of exogenous variables. We illustrate, in a full Bayesian and online context, the effectiveness of the methodology. We also measure its ability to identify turning points and to forecast the European business cycle on both realtime and last-revised data. Copyright © 2009 John Wiley & Sons, Ltd.

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File URL: http://hdl.handle.net/10.1002/for.1148
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Article provided by John Wiley & Sons, Ltd. in its journal Journal of Forecasting.

Volume (Year): 29 (2010)
Issue (Month): 1-2 ()
Pages: 145-167

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Handle: RePEc:jof:jforec:v:29:y:2010:i:1-2:p:145-167
DOI: 10.1002/for.1148
Contact details of provider: Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/2966

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  1. Simon M. Potter, 1993. "A Nonlinear Approach to U.S. GNP," UCLA Economics Working Papers 693, UCLA Department of Economics.
  2. Mark W. Watson, 1992. "Business Cycle Durations and Postwar Stabilization of the U.S. Economy," NBER Working Papers 4005, National Bureau of Economic Research, Inc.
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  12. Jacques Anas & Monica Billio & Laurent Ferrara & Gian Luigi Mazzi, 2008. "A System For Dating And Detecting Turning Points In The Euro Area," Manchester School, University of Manchester, vol. 76(5), pages 549-577, 09.
  13. Durbin, James & Koopman, Siem Jan, 2001. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, number 9780198523543, December.
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  18. Jasra, Ajay & Doucet, Arnaud, 2008. "Stability of sequential Monte Carlo samplers via the Foster-Lyapunov condition," Statistics & Probability Letters, Elsevier, vol. 78(17), pages 3062-3069, December.
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