Model averaging in Markov-switching models: Predicting national recessions with regional data

•We show how to perform dynamic model averaging with Markov-switching models.•We introduce combination weights based on the models' ability to fit a discrete outcome.•We combine forecasts from a large set of Markov-switching models to predict U.S. recessions.•Forecasts obtained from our new combination schemes outperform competitive benchmarks.

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.