This paper proposes the use of forecast combination to improve predictive accuracy
in forecasting the U.S. business cycle index, as published by the Business Cycle
Dating Committee of the NBER. It focuses on one-step ahead out-of-sample monthly
forecast utilising the well-established coincident indicators and yield curve models,
allowing for dynamics and real-time data revisions. Forecast combinations use logscore
and quadratic-score based weights, which change over time. This paper finds
that forecast accuracy improves when combining the probability forecasts of both the coincident indicators model and the yield curve model, compared to each model's
own forecasting performance.