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Predicting Recessions in Germany With Boosted Regression Trees

Author

Listed:
  • Jörg Döpke

    (Hochschule Merseburg (University of Applied Sciences Merseburg))

  • Ulrich Fritsche

    (Universität Hamburg (University of Hamburg))

  • Christian Pierdzioch

    (Helmut-Schmidt-Universität (Helmut-Schmidt-University))

Abstract
We use a machine-learning approach known as Boosted Regression Trees (BRT) to reexamine the usefulness of selected leading indicators for predicting recessions. We estimate the BRT approach on German data and study the relative importance of the indicators and their marginal effects on the probability of a recession. We then use receiver operating characteristic (ROC) curves to study the accuracy of forecasts. Results show that the short-term interest rate and the term spread are important leading indicators, but also that the stock market has some predictive value. The recession probability is a nonlinear function of these leading indicators. The BRT approach also helps to recover how the recession probability depends on the interactions of the leading indicators. While the predictive power of the short-term interest rates has declined over time, the term spread and the stock market have gained in importance. We also study how the shape of a forecaster’s utility function affects the optimal choice of a cutoff value above which the estimated recession prob- ability should be interpreted as a signal of a recession. The BRT approach shows a competitive out-of-sample performance compared to popular Pro- bit approaches.

Suggested Citation

  • Jörg Döpke & Ulrich Fritsche & Christian Pierdzioch, 2015. "Predicting Recessions in Germany With Boosted Regression Trees," Macroeconomics and Finance Series 201505, University of Hamburg, Department of Socioeconomics.
    • Handle: RePEc:hep:macppr:201505
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    File URL: https://www.wiso.uni-hamburg.de/repec/hepdoc/macppr_5_2015.pdf
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    References listed on IDEAS

    as
    1. Pierdzioch, Christian & Rülke, Jan-Christoph, 2015. "On the directional accuracy of forecasts of emerging market exchange rates," International Review of Economics & Finance, Elsevier, vol. 38(C), pages 369-376.
    2. Orphanides, Athanasios & Porter, Richard D., 2000. "P revisited: money-based inflation forecasts with a changing equilibrium velocity," Journal of Economics and Business, Elsevier, vol. 52(1-2), pages 87-100.
    3. Roger Farmer, 2012. "The Stock Market Crash of 2008 Caused the Great Recession," 2012 Meeting Papers 145, Society for Economic Dynamics.
    4. Sephton, Peter S., 1995. "Response surface estimates of the KPSS stationarity test," Economics Letters, Elsevier, vol. 47(3-4), pages 255-261, March.
    5. Klaus Wohlrabe & Teresa Buchen, 2014. "Assessing the Macroeconomic Forecasting Performance of Boosting: Evidence for the United States, the Euro Area and Germany," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(4), pages 231-242, July.
    6. Proaño, Christian R. & Theobald, Thomas, 2014. "Predicting recessions with a composite real-time dynamic probit model," International Journal of Forecasting, Elsevier, vol. 30(4), pages 898-917.
    7. Kwiatkowski, Denis & Phillips, Peter C. B. & Schmidt, Peter & Shin, Yongcheol, 1992. "Testing the null hypothesis of stationarity against the alternative of a unit root : How sure are we that economic time series have a unit root?," Journal of Econometrics, Elsevier, vol. 54(1-3), pages 159-178.
    8. Kholodilin Konstantin A., 2005. "Forecasting the German Cyclical Turning Points: Dynamic Bi-Factor Model with Markov Switching," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 225(6), pages 653-674, December.
    9. Beate Schirwitz, 2009. "A comprehensive German business cycle chronology," Empirical Economics, Springer, vol. 37(2), pages 287-301, October.
    10. Buchen, Teresa & Wohlrabe, Klaus, 2011. "Forecasting with many predictors: Is boosting a viable alternative?," Economics Letters, Elsevier, vol. 113(1), pages 16-18, October.
    11. Harding, Don & Pagan, Adrian, 2003. "A comparison of two business cycle dating methods," Journal of Economic Dynamics and Control, Elsevier, vol. 27(9), pages 1681-1690, July.
    12. Farmer, Roger E.A., 2012. "The stock market crash of 2008 caused the Great Recession: Theory and evidence," Journal of Economic Dynamics and Control, Elsevier, vol. 36(5), pages 693-707.
    13. Roberto Savona & Marika Vezzoli, 2015. "Fitting and Forecasting Sovereign Defaults using Multiple Risk Signals," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 77(1), pages 66-92, February.
    14. Travis J. Berge, 2015. "Predicting Recessions with Leading Indicators: Model Averaging and Selection over the Business Cycle," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(6), pages 455-471, September.
    15. Kaminsky, Graciela L., 2006. "Currency crises: Are they all the same?," Journal of International Money and Finance, Elsevier, vol. 25(3), pages 503-527, April.
    16. Drechsel, Katja & Scheufele, Rolf, 2012. "The performance of short-term forecasts of the German economy before and during the 2008/2009 recession," International Journal of Forecasting, Elsevier, vol. 28(2), pages 428-445.
    17. Graham Elliott & Ivana Komunjer & Allan Timmermann, 2008. "Biases in Macroeconomic Forecasts: Irrationality or Asymmetric Loss?," Journal of the European Economic Association, MIT Press, vol. 6(1), pages 122-157, March.
    18. Heikki Kauppi & Pentti Saikkonen, 2008. "Predicting U.S. Recessions with Dynamic Binary Response Models," The Review of Economics and Statistics, MIT Press, vol. 90(4), pages 777-791, November.
    19. Henri Nyberg, 2010. "Dynamic probit models and financial variables in recession forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 215-230.
    20. Travis J. Berge & Òscar Jordà, 2011. "Evaluating the Classification of Economic Activity into Recessions and Expansions," American Economic Journal: Macroeconomics, American Economic Association, vol. 3(2), pages 246-277, April.
    21. Mittnik, Stefan & Robinzonov, Nikolay & Spindler, Martin, 2015. "Stock market volatility: Identifying major drivers and the nature of their impact," Journal of Banking & Finance, Elsevier, vol. 58(C), pages 1-14.
    22. Malliaris, A.G. & Malliaris, Mary, 2015. "What drives gold returns? A decision tree analysis," Finance Research Letters, Elsevier, vol. 13(C), pages 45-53.
    23. Lahiri, Kajal & Wang, J. George, 2013. "Evaluating probability forecasts for GDP declines using alternative methodologies," International Journal of Forecasting, Elsevier, vol. 29(1), pages 175-190.
    24. 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.
    25. MacKinnon, James G, 1996. "Numerical Distribution Functions for Unit Root and Cointegration Tests," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(6), pages 601-618, Nov.-Dec..
    26. Savona, Roberto, 2014. "Hedge fund systemic risk signals," European Journal of Operational Research, Elsevier, vol. 236(1), pages 282-291.
    27. Gerhard Bry & Charlotte Boschan, 1971. "Programmed Selection of Cyclical Turning Points," NBER Chapters, in: Cyclical Analysis of Time Series: Selected Procedures and Computer Programs, pages 7-63, National Bureau of Economic Research, Inc.
    28. Thomas Theobald, 2012. "Combining Recession Probability Forecasts from a Dynamic Probit Indicator," IMK Working Paper 89-2012, IMK at the Hans Boeckler Foundation, Macroeconomic Policy Institute.
    29. Konstantin A. Kholodilin, 2005. "Forecasting the Turns of German Business Cycle: Dynamic Bi-factor Model with Markov Switching," Discussion Papers of DIW Berlin 494, DIW Berlin, German Institute for Economic Research.
    30. Duttagupta, Rupa & Cashin, Paul, 2011. "Anatomy of banking crises in developing and emerging market countries," Journal of International Money and Finance, Elsevier, vol. 30(2), pages 354-376, March.
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    Cited by:

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    2. Shahram Fattahi & Kiomars Sohaili & Hamed Monkaresi & Fatemeh Mehrabi, 2017. "Modelling and Forecasting Recessions in Oil-exporting Countries: The Case of Iran," International Journal of Economics and Financial Issues, Econjournals, vol. 7(3), pages 569-574.
    3. Proaño, Christian R. & Tarassow, Artur, 2018. "Evaluating the predicting power of ordered probit models for multiple business cycle phases in the U.S. and Japan," Journal of the Japanese and International Economies, Elsevier, vol. 50(C), pages 60-71.
    4. Emrich Eike & Pierdzioch Christian, 2016. "Public Goods, Private Consumption, and Human Capital: Using Boosted Regression Trees to Model Volunteer Labour Supply," Review of Economics, De Gruyter, vol. 67(3), pages 263-283, December.
    5. Daniel Wochner, 2020. "Dynamic Factor Trees and Forests – A Theory-led Machine Learning Framework for Non-Linear and State-Dependent Short-Term U.S. GDP Growth Predictions," KOF Working papers 20-472, KOF Swiss Economic Institute, ETH Zurich.

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    More about this item

    Keywords

    Recession forecasting; Boosting; Regression trees; ROC curves;
    All these keywords.

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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