Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
This paper introduces new weighting schemes for model averaging when one is interested in combining discrete forecasts from competing Markov-switching models. In the empirical application, we forecast U.S. business cycle turning points with state-level employment data. We find that forecasts obtained with our best combination scheme provide timely updates of U.S. recessions in that they outperform a notoriously difficult benchmark to beat (the anxious index from the Survey of Professional Forecasters) for short-term forecasts.