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Combination schemes for turning point predictions

  • Billio, Monica
  • Casarin, Roberto
  • Ravazzolo, Francesco
  • van Dijk, Herman K.

We propose new forecast combination schemes for predicting turning points of business cycles. The proposed combination schemes are based on the forecasting performances of a given set of models with the aim to provide better turning point predictions. In particular, we consider predictions generated by autoregressive (AR) and Markov-switching AR models, which are commonly used for business cycle analysis. In order to account for parameter uncertainty we consider a Bayesian approach for both estimation and prediction and compare, in terms of statistical accuracy, the individual models and the combined turning point predictions for the United States and the Euro area business cycles.

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File URL: http://www.sciencedirect.com/science/article/pii/S106297691200052X
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Article provided by Elsevier in its journal The Quarterly Review of Economics and Finance.

Volume (Year): 52 (2012)
Issue (Month): 4 ()
Pages: 402-412

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Handle: RePEc:eee:quaeco:v:52:y:2012:i:4:p:402-412
DOI: 10.1016/j.qref.2012.08.002
Contact details of provider: Web page: http://www.elsevier.com/locate/inca/620167

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  1. Christian Francq & Jean-Michel Zakoïan, 2000. "Stationarity of Multivariate Markov-Switching ARMA Models," Working Papers 2000-32, Centre de Recherche en Economie et Statistique.
  2. Michael Dueker, 2004. "Non-Markovian Regime Switching with Endogenous States and Time-Varying State Strengths," Econometric Society 2004 Latin American Meetings 34, Econometric Society.
  3. Gerhard Bry & Charlotte Boschan, 1971. "Foreword to "Cyclical Analysis of Time Series: Selected Procedures and Computer Programs"," NBER Chapters, in: Cyclical Analysis of Time Series: Selected Procedures and Computer Programs, pages -1 National Bureau of Economic Research, Inc.
  4. Phillips, P C B, 1991. "Bayesian Routes and Unit Roots: De Rebus Prioribus Semper Est Disputandum," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 6(4), pages 435-73, Oct.-Dec..
  5. Fabio Canova & Matteo Ciccarelli, 1999. "Forecasting and turning point predictions in a Bayesian panel VAR model," Economics Working Papers 443, Department of Economics and Business, Universitat Pompeu Fabra.
  6. L. Wasserman, 2000. "Asymptotic inference for mixture models by using data-dependent priors," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(1), pages 159-180.
  7. John Geweke, 1999. "Using simulation methods for bayesian econometric models: inference, development,and communication," Econometric Reviews, Taylor & Francis Journals, vol. 18(1), pages 1-73.
  8. Monica Billio & Jacques Anas & Laurent Ferrara & Marco Lo Duca, 2007. "Business Cycle Analysis with Multivariate Markov Switching Models," Working Papers 2007_32, Department of Economics, University of Venice "Ca' Foscari".
  9. Simon M. Potter, 1993. "A Nonlinear Approach to U.S. GNP," UCLA Economics Working Papers 693, UCLA Department of Economics.
  10. Hilde C. Bjørnland & Karsten R. Gerdrup & Anne Sofie Jore & Leif Anders Thorsrud & Christie Smith, 2010. "Does forecast combination improve Norges Bank inflation forecasts?," Working Papers 0002, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
  11. Terui, N. & van Dijk, H.K., 1999. "Combined forecasts from linear and nonlinear time series models," Econometric Institute Research Papers EI 9949-/A, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  12. Filardo, Andrew J, 1994. "Business-Cycle Phases and Their Transitional Dynamics," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(3), pages 299-308, July.
  13. Durland, J Michael & McCurdy, Thomas H, 1994. "Duration-Dependent Transitions in a Markov Model of U.S. GNP Growth," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(3), pages 279-88, July.
  14. Koop, Gary & Osiewalski, Jacek & Steel, Mark F. J., 1995. "Bayesian long-run prediction in time series models," Journal of Econometrics, Elsevier, vol. 69(1), pages 61-80, September.
  15. Clements, Michael P. & Harvey, David I., 2011. "Combining probability forecasts," International Journal of Forecasting, Elsevier, vol. 27(2), pages 208-223, April.
  16. Francis X. Diebold & Glenn D. Rudebusch & Daniel E. Sichel, 1991. "Further evidence on business cycle duration dependence," Working Papers 91-11, Federal Reserve Bank of Philadelphia.
  17. Diebold, Francis X & Rudebusch, Glenn D, 1996. "Measuring Business Cycles: A Modern Perspective," The Review of Economics and Statistics, MIT Press, vol. 78(1), pages 67-77, February.
  18. Massimiliano Caporin & Domenico Sartore, 2006. "Methodological aspects of time series back-calculation," Working Papers 2006_56, Department of Economics, University of Venice "Ca' Foscari".
  19. Goldfeld, Stephen M. & Quandt, Richard E., 1973. "A Markov model for switching regressions," Journal of Econometrics, Elsevier, vol. 1(1), pages 3-15, March.
  20. Sims, Christopher A & Uhlig, Harald, 1991. "Understanding Unit Rooters: A Helicopter Tour," Econometrica, Econometric Society, vol. 59(6), pages 1591-99, November.
  21. Chang-Jin Kim & Charles R. Nelson, 1999. "State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262112388, March.
  22. Chauvet, Marcelle & Piger, Jeremy, 2008. "A Comparison of the Real-Time Performance of Business Cycle Dating Methods," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 42-49, January.
  23. Jeremy J. Nalewaik, 2012. "Estimating Probabilities of Recession in Real Time Using GDP and GDI," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 44(1), pages 235-253, 02.
  24. de Pooter, M.D. & Ravazzolo, F. & Segers, R. & van Dijk, H.K., 2008. "Bayesian near-boundary analysis in basic macroeconomic time series models," Econometric Institute Research Papers EI 2008-13, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  25. 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.
  26. McCulloch, Robert E. & Tsay, Ruey S., 1994. "Bayesian Inference of Trend and Difference-Stationarity," Econometric Theory, Cambridge University Press, vol. 10(3-4), pages 596-608, August.
  27. Don Harding & Adrian Pagan, 2000. "Disecting the Cycle: A Methodological Investigation," Econometric Society World Congress 2000 Contributed Papers 1164, Econometric Society.
  28. Andrew Ang & Geert Bekaert, 1998. "Regime Switches in Interest Rates," NBER Working Papers 6508, National Bureau of Economic Research, Inc.
  29. Mönch, Emanuel & Uhlig, Harald, 2004. "Towards a Monthly Business Cycle Chronology for the Euro Area," CEPR Discussion Papers 4377, C.E.P.R. Discussion Papers.
  30. J. Vermaak & C. Andrieu & A. Doucet & S. J. Godsill, 2004. "Reversible Jump Markov Chain Monte Carlo Strategies for Bayesian Model Selection in Autoregressive Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 25(6), pages 785-809, November.
  31. Geweke, John & Whiteman, Charles, 2006. "Bayesian Forecasting," Handbook of Economic Forecasting, Elsevier.
  32. Billio Monica & Casarin Roberto, 2011. "Beta Autoregressive Transition Markov-Switching Models for Business Cycle Analysis," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 15(4), pages 1-32, September.
  33. Monica Billio & Jacques Anas & Laurent Ferrara & Marco Lo Duca, 2007. "A turning point chronology for the Euro-zone," Working Papers 2007_33, Department of Economics, University of Venice "Ca' Foscari".
  34. James H. Stock & Mark W. Watson, 2010. "Indicators for Dating Business Cycles: Cross-History Selection and Comparisons," American Economic Review, American Economic Association, vol. 100(2), pages 16-19, May.
  35. Lennart Hoogerheide & Richard Kleijn & Francesco Ravazzolo & Herman K. van Dijk & Marno Verbeek, 2009. "Forecast Accuracy and Economic Gains from Bayesian Model Averaging using Time Varying Weights," Tinbergen Institute Discussion Papers 09-061/4, Tinbergen Institute.
  36. Emanuel Mönch & Harald Uhlig, 2003. "Towards a Monthly Business Cycle Chronology for the Euro Area," SFB 649 Discussion Papers SFB649DP2005-023, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany, revised Apr 2005.
  37. Massimo Guidolin, 2011. "Markov Switching Models in Empirical Finance," Working Papers 415, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
  38. Zellner, Arnold & Hong, Chansik & Min, Chung-ki, 1991. "Forecasting turning points in international output growth rates using Bayesian exponentially weighted autoregression, time-varying parameter, and pooling techniques," Journal of Econometrics, Elsevier, vol. 49(1-2), pages 275-304.
  39. Gerhard Bry & Charlotte Boschan, 1971. "Cyclical Analysis of Time Series: Selected Procedures and Computer Programs," NBER Books, National Bureau of Economic Research, Inc, number bry_71-1, September.
  40. Min, Chung-ki & Zellner, Arnold, 1993. "Bayesian and non-Bayesian methods for combining models and forecasts with applications to forecasting international growth rates," Journal of Econometrics, Elsevier, vol. 56(1-2), pages 89-118, March.
  41. Jochmann, Markus & Koop, Gary & Strachan, Rodney W., 2010. "Bayesian forecasting using stochastic search variable selection in a VAR subject to breaks," International Journal of Forecasting, Elsevier, vol. 26(2), pages 326-347, April.
  42. James D. Hamilton, 2010. "Calling Recessions in Real Time," NBER Working Papers 16162, National Bureau of Economic Research, Inc.
  43. Sims, Christopher A., 1988. "Bayesian skepticism on unit root econometrics," Journal of Economic Dynamics and Control, Elsevier, vol. 12(2-3), pages 463-474.
  44. John Geweke, 1999. "Using Simulation Methods for Bayesian Econometric Models," Computing in Economics and Finance 1999 832, Society for Computational Economics.
  45. Kim, C-J., 1991. "Dynamic Linear Models with Markov-Switching," Papers 91-8, York (Canada) - Department of Economics.
  46. Daniel E. Sichel, 1989. "Business cycle duration dependence: a parametric approach," Working Paper Series / Economic Activity Section 98, Board of Governors of the Federal Reserve System (U.S.).
  47. Monica Billio & Roberto Casarin, 2010. "Identifying business cycle turning points with sequential Monte Carlo methods: an online and real-time application to the Euro area," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 145-167.
  48. Albert, James H & Chib, Siddhartha, 1993. "Bayes Inference via Gibbs Sampling of Autoregressive Time Series Subject to Markov Mean and Variance Shifts," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(1), pages 1-15, January.
  49. Chang-Jin Kim & Christian J. Murray, 2002. "Permanent and transitory components of recessions," Empirical Economics, Springer, vol. 27(2), pages 163-183.
  50. 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.
  51. Hans-Martin Krolzig, 2000. "Predicting Markov-Switching Vector Autoregressive Processes," Economics Series Working Papers 2000-W31, University of Oxford, Department of Economics.
  52. 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.
  53. Michael P. Clements & Hans-Martin Krolzig, 1998. "A comparison of the forecast performance of Markov-switching and threshold autoregressive models of US GNP," Econometrics Journal, Royal Economic Society, vol. 1(Conferenc), pages C47-C75.
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