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

  • Travis Berge

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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File URL: http://www.kansascityfed.org/publicat/reswkpap/pdf/rwp13-05.pdf
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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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  1. 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.
  2. 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.).
  3. Giacomini, Raffaella & White, Halbert, 2003. "Tests of Conditional Predictive Ability," University of California at San Diego, Economics Working Paper Series qt5jk0j5jh, Department of Economics, UC San Diego.
  4. Gernot Doppelhofer & Ronald I. Miller & Xavier Sala-i-Martin, 2000. "Determinants of Long-Term Growth: A Bayesian Averaging of Classical Estimates (BACE) Approach," NBER Working Papers 7750, National Bureau of Economic Research, Inc.
  5. 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.).
  6. 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.
  7. Rudebusch, Glenn D. & Williams, John C., 2009. "Forecasting Recessions: The Puzzle of the Enduring Power of the Yield Curve," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 492-503.
  8. David Hendry & Michael Clements, 2001. "Pooling of Forecasts," Economics Series Working Papers 2002-W09, University of Oxford, Department of Economics.
  9. Timmermann, Allan G, 2005. "Forecast Combinations," CEPR Discussion Papers 5361, C.E.P.R. Discussion Papers.
  10. Marcelle Chauvet & Simon Potter, 2005. "Forecasting recessions using the yield curve," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(2), pages 77-103.
  11. repec:ecl:ucdeco:09-18 is not listed on IDEAS
  12. Mark W. Watson & James H. Stock, 2004. "Combination forecasts of output growth in a seven-country data set," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(6), pages 405-430.
  13. Elliott, Graham & Lieli, Robert P., 2013. "Predicting binary outcomes," Journal of Econometrics, Elsevier, vol. 174(1), pages 15-26.
  14. 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.
  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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