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


  • Travis J. 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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  • Travis J. Berge, 2013. "Predicting recessions with leading indicators: model averaging and selection over the business cycle," Research Working Paper RWP 13-05, Federal Reserve Bank of Kansas City.
  • Handle: RePEc:fip:fedkrw:rwp13-05

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    13. Chauvet, Marcelle & Senyuz, Zeynep, 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.).
    14. 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.
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    1. repec:eee:ecolet:v:157:y:2017:i:c:p:45-49 is not listed on IDEAS
    2. repec:spr:empeco:v:53:y:2017:i:1:d:10.1007_s00181-016-1200-7 is not listed on IDEAS
    3. repec:eee:phsmap:v:489:y:2018:i:c:p:102-111 is not listed on IDEAS
    4. Vasilios Plakandaras & Juncal Cunado & Rangan Gupta & Mark E. Wohar, 2016. "Do Leading Indicators Forecast U.S. Recessions? A Nonlinear Re-Evaluation Using Historical Data," Working Papers 201685, University of Pretoria, Department of Economics.
    5. Giusto, Andrea & Piger, Jeremy, 2017. "Identifying business cycle turning points in real time with vector quantization," International Journal of Forecasting, Elsevier, vol. 33(1), pages 174-184.
    6. Guérin, Pierre & Leiva-Leon, Danilo, 2017. "Model averaging in Markov-switching models: Predicting national recessions with regional data," Economics Letters, Elsevier, vol. 157(C), pages 45-49.
    7. Herman O. Stekler & Tianyu Ye, 2016. "Evaluating a Leading Indicator: An Application: the Term Spread," Working Papers 2016-004, The George Washington University, Department of Economics, Research Program on Forecasting.
    8. repec:eco:journ1:2017-03-75 is not listed on IDEAS
    9. Döpke, Jörg & Fritsche, Ulrich & Pierdzioch, Christian, 2017. "Predicting recessions with boosted regression trees," International Journal of Forecasting, Elsevier, vol. 33(4), pages 745-759.
    10. Davig, Troy A. & Smalter Hall, Aaron, 2016. "Recession forecasting using Bayesian classification," Research Working Paper RWP 16-6, Federal Reserve Bank of Kansas City, revised 01 Feb 2017.
    11. Stefan Gebauer, 2017. "The Use of Financial Market Variables in Forecasting," DIW Roundup: Politik im Fokus 115, DIW Berlin, German Institute for Economic Research.
    12. Jörg Döpke & Ulrich Fritsche & Christian Pierdzioch, 2015. "Predicting Recessions in Germany With Boosted Regression Trees," Macroeconomics and Finance Series 201505, Hamburg University, Department Wirtschaft und Politik.

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    Recessions ; Economic indicators ; Business cycles;

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