Predicting Recessions With Boosted Regression Trees
Ulrich Fritsche () and
No 2015-004, Working Papers from The George Washington University, Department of Economics, Research Program on Forecasting
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
Keywords: : Recession forecasting; Boosting; Regression trees; ROC analysis (search for similar items in EconPapers)
JEL-codes: C52 C53 E32 E37 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-for, nep-mac and nep-pke
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Journal Article: Predicting recessions with boosted regression trees (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:gwc:wpaper:2015-004
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