Predicting recessions with boosted regression trees

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. Our results show that measures of the short-term interest rate and the term spread are important leading indicators. The recession probability is a nonlinear function of these leading indicators. The BRT approach also helps to uncover the way in which the recession probability depends on the interactions between 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. The BRT approach shows a better out-of-sample performance than popular probit approaches.