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

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  • Monica Billio
  • Roberto Casarin

Abstract

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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Bibliographic Info

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

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Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/2966

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Cited by:
  1. Monica Billio & Roberto Casarin & Francesco Ravazzolo & Herman K. van Dijk, 2010. "Combining predictive densities using Bayesian filtering with applications to US economics data," Working Paper 2010/29, Norges Bank.
  2. Monica Billio & Roberto Casarin & Francesco Ravazzolo & Herman K. van Dijk, 2012. "Time-varying Combinations of Predictive Densities using Nonlinear Filtering," Tinbergen Institute Discussion Papers 12-118/III, Tinbergen Institute.
  3. Monica Billio & Roberto Casarin & Francesco Ravazzolo & Herman K. van Dijk, 2011. "Combination Schemes for Turning Point Predictions," Tinbergen Institute Discussion Papers 11-123/4, Tinbergen Institute.
  4. Knut Are Aastveit & Tørres G. Trovik, 2008. "Estimating the output gap in real time: A factor model approach," Working Paper 2008/23, Norges Bank.
  5. Peter Martey Addo & Monica Billio & Dominique Guegan, 2013. "Turning point chronology for the Euro-Zone: A Distance Plot Approach," Documents de travail du Centre d'Economie de la Sorbonne 13025r, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne, revised Oct 2013.
  6. Monica Billio & Roberto Casarin & Francesco Ravazzolo & Herman K. van Dijk, 2011. "Combining Predictive Densities using Bayesian Filtering with Applications to US Economics Data," Tinbergen Institute Discussion Papers 11-003/4, Tinbergen Institute.
  7. Monica Billio & Roberto Casarin & Francesco Ravazzolo & Herman K. van Dijk, 2011. "Combining Predictive Densities using Nonlinear Filtering with Applications to US Economics Data," Tinbergen Institute Discussion Papers 11-172/4, Tinbergen Institute.
  8. Monica Billio & Roberto Casarin & Francesco Ravazzolo & Herman K. van Dijk, 2012. "Time-varying Combinations of Predictive Densities using Nonlinear Filtering," Tinbergen Institute Discussion Papers 12-118/III, Tinbergen Institute.
  9. Monica Billio & Roberto Casarin & Francesco Ravazzolo & Herman K. van Dijk, 2011. "Combining Predictive Densities using Nonlinear Filtering with Applications to US Economics Data," Tinbergen Institute Discussion Papers 11-172/4, Tinbergen Institute.
  10. Monica Billio & Roberto Casarin & Francesco Ravazzolo & Herman K. van Dijk, 2011. "Combination Schemes for Turning Point Predictions," Tinbergen Institute Discussion Papers 11-123/4, Tinbergen Institute.
  11. 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.

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