Forecasting recessions in Germany with machine learning

This paper applies machine learning to forecast business cycles in the German economy using a high-dimensional dataset with 73 indicators, primarily from the OECD Main Economic Indicator Database, covering a time period from 1973 to 2023. Sequential Floating Forward Selection (SFFS) is used to select the most relevant indicators and build compact, explainable, and performant models. Therefore, regularized regression models (LASSO, Ridge) and tree-based classification models (Random Forest, and Logit Boost) are used as challenger models to outperform a probit model containing the term spread as a predictor. All models are trained on data from 1973-2006 and evaluated on a hold-out-sample starting in 2006. The study reveals that fewer indicators are necessary to model recessions. Models built with SFFS have a maximum of eleven indicators. Furthermore, the study setting shows that many indicators are stable across time and business cycles. Machine learning models prove particularly effective in predicting recessions during periods of quantitative easing, when the predictive power of the term spread diminishes. The findings contribute to the ongoing discussion on the use of machine learning in economic forecasting, especially in the context of limited and imbalanced data.