Model averaging in markov-switching models: predicting national recessions with regional data
Pierre Guérin () and
Danilo Leiva-Leon ()
No 1727, Working Papers from Banco de España, Working Papers Homepage
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 statelevel 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 dicult benchmark to beat (the anxious index from the Survey of Professional Forecasters) for short-term forecasts.
Keywords: business cycles; forecast combination; forecasting; Markov-switching; nowcasting (search for similar items in EconPapers)
JEL-codes: C53 E32 E37 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ets, nep-for, nep-mac, nep-ore and nep-ure
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http://www.bde.es/f/webbde/SES/Secciones/Publicaci ... /17/Fich/dt1727e.pdf First version, July 2017 (application/pdf)
Journal Article: Model averaging in Markov-switching models: Predicting national recessions with regional data (2017)
Working Paper: Model Averaging in Markov-Switching Models: Predicting National Recessions with Regional Data (2015)
Working Paper: Model Averaging in Markov-Switching Models: Predicting National Recessions with Regional Data (2014)
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Persistent link: https://EconPapers.repec.org/RePEc:bde:wpaper:1727
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