Predicting Recessions with Leading Indicators : Model Averaging and Selection Over the Business Cycle

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