Do leading indicators forecast U.S. recessions? A nonlinear re-evaluation using historical data

Abstract

This paper analyses to what extent a selection of leading indicators is able to forecast U.S. recessions, by means of both dynamic probit models and Support Vector Machine (SVM) models, using monthly data from January 1871 to June 2016. The results suggest that the probit models predict U.S. recession periods more accurately than SVM models up to six months ahead, while the SVM models are more accurate over longer horizons. Furthermore, SVM models appear to distinguish between recessions and tranquil periods better than probit models do. Finally, the most accurate forecasting models are those that include oil, stock returns and the term spread as leading indicators.