Predicting Recessions with Leading Indicators: Model Averaging and Selection Over the Business Cycle
Posted: 9 Aug 2013 Last revised: 28 Nov 2015
Date Written: January 2014
Abstract
Four model selection methods are applied to the problem of predicting business cycle turning points: equally-weighted forecasts, Bayesian model averaged forecasts, and two models produced by the machine learning algorithm boosting. The model selection algorithms condition on different economic indicators at different forecast horizons. Models produced by BMA and boosting outperform equally-weighted forecasts, even out of sample. Nonlinear models also appear to outperform their linear counterparts. Although the forecast ability of the yield curve endures, additional conditioning variables improves forecast ability. The findings highlight several important features of the business cycle.
Keywords: Business cycle turning points; recessions; variable selection; boosting; Bayesian model averaging; probabilistic forecasts
JEL Classification: C4, C5, C25, C53, E32
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