IDEAS home Printed from https://ideas.repec.org/
MyIDEAS: Log in (now much improved!) to save this paper

Predicting Recessions With Boosted Regression Trees

Listed author(s):
  • Jörg Döpke

    ()

    (University of Applied Sciences Merseburg)

  • Ulrich Fritsche

    ()

    (University Hamburg)

  • Christian Pierdzioch

    ()

    (Helmut-Schmidt-University Hamburg)

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 probability should be interpreted as a signal of a recession. The BRT approach shows a competitive out-of-sample performance compared to popular Probit approaches

If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

File URL: https://www2.gwu.edu/~forcpgm/2015-004.pdf
File Function: First version, 2015
Download Restriction: no

Paper provided by The George Washington University, Department of Economics, Research Program on Forecasting in its series Working Papers with number 2015-004.

as
in new window

Length: 51 pages
Date of creation: Dec 2015
Handle: RePEc:gwc:wpaper:2015-004
Contact details of provider: Postal:
Monroe Hall #340, 2115 G Street, NW, Washington, DC 20052

Phone: (202) 994-6150
Fax: (202) 994-6147
Web page: https://www2.gwu.edu/~forcpgm
Email:


More information through EDIRC

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:

as
in new window


  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. 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.
  5. Arturo Estrella & Frederic S. Mishkin, 1998. "Predicting U.S. Recessions: Financial Variables As Leading Indicators," The Review of Economics and Statistics, MIT Press, vol. 80(1), pages 45-61, February.
  6. Anatol Balbach & Denis S. Karnosky, 1975. "Real money balances: a good forecasting device and a good policy target?," Review, Federal Reserve Bank of St. Louis, issue Sep, pages 11-15.
  7. 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.
  8. Artis, Michael J & Kontolemis, Zenon G & Osborn, Denise R, 1997. "Business Cycles for G7 and European Countries," The Journal of Business, University of Chicago Press, vol. 70(2), pages 249-279, April.
  9. 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.
  10. Ferrara, Laurent & Marcellino, Massimiliano & Mogliani, Matteo, 2015. "Macroeconomic forecasting during the Great Recession: The return of non-linearity?," International Journal of Forecasting, Elsevier, vol. 31(3), pages 664-679.
  11. 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.
  12. Serena Ng, 2014. "Viewpoint: Boosting Recessions," Canadian Journal of Economics, Canadian Economics Association, vol. 47(1), pages 1-34, February.
  13. Lessmann, Stefan & Sung, Ming-Chien & Johnson, Johnnie E.V., 2010. "Alternative methods of predicting competitive events: An application in horserace betting markets," International Journal of Forecasting, Elsevier, vol. 26(3), pages 518-536, July.
  14. Ben Taieb, Souhaib & Hyndman, Rob J., 2014. "A gradient boosting approach to the Kaggle load forecasting competition," International Journal of Forecasting, Elsevier, vol. 30(2), pages 382-394.
  15. Manasse, Paolo & Roubini, Nouriel, 2009. ""Rules of thumb" for sovereign debt crises," Journal of International Economics, Elsevier, vol. 78(2), pages 192-205, July.
  16. Buchen, Teresa & Wohlrabe, Klaus, 2011. "Forecasting with many predictors: Is boosting a viable alternative?," Economics Letters, Elsevier, vol. 113(1), pages 16-18, October.
  17. 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.
  18. 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.
  19. Baker, Stuart G. & Kramer, Barnett S., 2007. "Peirce, Youden, and Receiver Operating Characteristic Curves," The American Statistician, American Statistical Association, vol. 61, pages 343-346, November.
  20. Arturo Estrella & Anthony P. Rodrigues & Sebastian Schich, 2003. "How Stable is the Predictive Power of the Yield Curve? Evidence from Germany and the United States," The Review of Economics and Statistics, MIT Press, vol. 85(3), pages 629-644, August.
  21. 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.
  22. Arthur F. Burns & Wesley C. Mitchell, 1946. "Measuring Business Cycles," NBER Books, National Bureau of Economic Research, Inc, number burn46-1, November.
  23. 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.
  24. Jörg Döpke & Ulrich Fritsche & Boriss Siliverstovs, 2010. "Evaluating German business cycle forecasts under an asymmetric loss function," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2010(1), pages 1-18.
  25. Lloyd, James Robert, 2014. "GEFCom2012 hierarchical load forecasting: Gradient boosting machines and Gaussian processes," International Journal of Forecasting, Elsevier, vol. 30(2), pages 369-374.
  26. Cáceres, Neila & Malone, Samuel W., 2013. "Forecasting leadership transitions around the world," International Journal of Forecasting, Elsevier, vol. 29(4), pages 575-591.
  27. 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.
  28. Duarte, Agustin & Venetis, Ioannis A. & Paya, Ivan, 2005. "Predicting real growth and the probability of recession in the Euro area using the yield spread," International Journal of Forecasting, Elsevier, vol. 21(2), pages 261-277.
  29. 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.
  30. 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, 03.
  31. Silva, Lucas, 2014. "A feature engineering approach to wind power forecasting," International Journal of Forecasting, Elsevier, vol. 30(2), pages 395-401.
  32. 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.
  33. Nikolay Robinzonov & Gerhard Tutz & Torsten Hothorn, 2012. "Boosting techniques for nonlinear time series models," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 96(1), pages 99-122, January.
  34. 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.
  35. 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.
  36. Bec, Frédérique & Bouabdallah, Othman & Ferrara, Laurent, 2014. "The way out of recessions: A forecasting analysis for some Euro area countries," International Journal of Forecasting, Elsevier, vol. 30(3), pages 539-549.
  37. Schneider, Matthew J. & Gorr, Wilpen L., 2015. "ROC-based model estimation for forecasting large changes in demand," International Journal of Forecasting, Elsevier, vol. 31(2), pages 253-262.
  38. 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.
  39. Dean Croushore, 2011. "Frontiers of Real-Time Data Analysis," Journal of Economic Literature, American Economic Association, vol. 49(1), pages 72-100, March.
  40. Kim, Hyun Hak & Swanson, Norman R., 2014. "Forecasting financial and macroeconomic variables using data reduction methods: New empirical evidence," Journal of Econometrics, Elsevier, vol. 178(P2), pages 352-367.
  41. Bluedorn, John C. & Decressin, Jörg & Terrones, Marco E., 2016. "Do asset price drops foreshadow recessions?," International Journal of Forecasting, Elsevier, vol. 32(2), pages 518-526.
  42. Malliaris, A.G. & Malliaris, Mary, 2015. "What drives gold returns? A decision tree analysis," Finance Research Letters, Elsevier, vol. 13(C), pages 45-53.
  43. Estrella, Arturo & Hardouvelis, Gikas A, 1991. " The Term Structure as a Predictor of Real Economic Activity," Journal of Finance, American Finance Association, vol. 46(2), pages 555-576, June.
  44. Liu, Weiling & Moench, Emanuel, 2016. "What predicts US recessions?," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1138-1150.
  45. Brand, Claus & Reimers, Hans-Eggert & Seitz, Franz, 2003. "Forecasting real GDP: what role for narrow money?," Working Paper Series 254, European Central Bank.
  46. Friedman, Jerome H., 2002. "Stochastic gradient boosting," Computational Statistics & Data Analysis, Elsevier, vol. 38(4), pages 367-378, February.
  47. Ivanova, Detelina & Lahiri, Kajal & Seitz, Franz, 2000. "Interest rate spreads as predictors of German inflation and business cycles," International Journal of Forecasting, Elsevier, vol. 16(1), pages 39-58.
  48. 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.
  49. 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.
  50. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-438, July.
  51. Jushan Bai & Serena Ng, 2009. "Boosting diffusion indices," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(4), pages 607-629.
  52. 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..
  53. Savona, Roberto, 2014. "Hedge fund systemic risk signals," European Journal of Operational Research, Elsevier, vol. 236(1), pages 282-291.
  54. 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.
  55. 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.
  56. 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.
  57. Robert J. Barro & José F. Ursúa, 2009. "Stock-Market Crashes and Depressions," NBER Working Papers 14760, National Bureau of Economic Research, Inc.
  58. Breitung, Jorg & Candelon, Bertrand, 2006. "Testing for short- and long-run causality: A frequency-domain approach," Journal of Econometrics, Elsevier, vol. 132(2), pages 363-378, June.
Full references (including those not matched with items on IDEAS)

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

When requesting a correction, please mention this item's handle: RePEc:gwc:wpaper:2015-004. See general information about how to correct material in RePEc.

For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Tara M. Sinclair)

If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

If references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

Please note that corrections may take a couple of weeks to filter through the various RePEc services.

This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.