Model Averaging in Markov-Switching Models : Predicting National Recessions with Regional Data

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

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Year of publication:	2017
Authors:	Guérin, Pierre
Other Persons:	Leiva‐Leon, Danilo (contributor)
Publisher:	[2017]: [S.l.] : SSRN
Subject:	Markov-Kette \| Markov chain \| Prognoseverfahren \| Forecasting model \| Konjunktur \| Business cycle \| Frühindikator \| Leading indicator \| Schätzung \| Estimation \| Theorie \| Theory \| Bayes-Statistik \| Bayesian inference

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