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Predicting recessions with leading indicators: model averaging and selection over the business cycle

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  • Travis Berge

Abstract

This paper evaluates the ability of several commonly followed economic indicators to predict business cycle turning points. As a baseline, forecasts from univariate models are combined by taking averages or by weighting forecasts with model-implied posterior probabilities. These combined forecasts are compared to those from a sophisticated model selection algorithm that allows for nonlinear model speci_cations. The preferred forecasting model is one that allows for nonlinear behavior across the business cycle and combines information from the yield curve with other indicators, especially at very short and very long horizons.

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Bibliographic Info

Paper provided by Federal Reserve Bank of Kansas City in its series Research Working Paper with number RWP 13-05.

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Date of creation: 2013
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Handle: RePEc:fip:fedkrw:rwp13-05

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Keywords: Recessions ; Economic indicators ; Business cycles;

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  1. Raffaella Giacomini & Halbert White, 2003. "Tests of Conditional Predictive Ability," Econometrics 0308001, EconWPA.
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  3. repec:ecl:ucdeco:09-18 is not listed on IDEAS
  4. William A. Brock & Steven N. Durlauf & Kenneth D. West, 2004. "Model Uncertainty and Policy Evaluation: Some Theory and Empirics," NBER Working Papers 10916, National Bureau of Economic Research, Inc.
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  7. Marcelle Chauvet & Zeynep Senyuz, 2012. "A dynamic factor model of the yield curve as a predictor of the economy," Finance and Economics Discussion Series 2012-32, Board of Governors of the Federal Reserve System (U.S.).
  8. Elliott, Graham & Lieli, Robert P., 2013. "Predicting binary outcomes," Journal of Econometrics, Elsevier, vol. 174(1), pages 15-26.
  9. Marco Aiolfi & Carlos Capistrán & Allan Timmermann, 2010. "Forecast Combinations," Working Papers 2010-04, Banco de México.
  10. Michael Dueker, 2005. "Dynamic Forecasts of Qualitative Variables: A Qual VAR Model of U.S. Recessions," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 96-104, January.
  11. Estrella, Arturo, 1998. "A New Measure of Fit for Equations with Dichotomous Dependent Variables," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(2), pages 198-205, April.
  12. Glenn D. Rudebusch & John C. Williams, 2007. "Forecasting recessions: the puzzle of the enduring power of the yield curve," Working Paper Series 2007-16, Federal Reserve Bank of San Francisco.
  13. David Hendry & Michael Clements, 2001. "Pooling of Forecasts," Economics Series Working Papers 2002-W09, University of Oxford, Department of Economics.
  14. Jonathan H. Wright, 2006. "The yield curve and predicting recessions," Finance and Economics Discussion Series 2006-07, Board of Governors of the Federal Reserve System (U.S.).
  15. James Morley & Jeremy Piger, 2012. "The Asymmetric Business Cycle," The Review of Economics and Statistics, MIT Press, vol. 94(1), pages 208-221, February.
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