Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/123763
Authors: 
Guérin, Pierre
Leiva-Leon, Danilo
Year of Publication: 
2015
Series/Report no.: 
Bank of Canada Working Paper 2015-24
Abstract: 
This paper introduces new weighting schemes for model averaging when one is interested in combining discrete forecasts from competing Markov-switching models. In particular, we extend two existing classes of combination schemes - Bayesian (static) model averaging and dynamic model averaging - so as to explicitly reflect the objective of forecasting a discrete outcome. Both simulation and empirical exercises show that our new combination schemes outperform competing combination schemes in terms of forecasting accuracy. In the empirical application, we estimate and 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 the U.S. business cycles.
Subjects: 
Business fluctuations and cycles
Econometric and statistical methods
JEL: 
C53
E32
E37
Document Type: 
Working Paper

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