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

Pierre Guérin and Danilo Leiva-Leon ()

Staff Working Papers from Bank of Canada

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.

Keywords: Business fluctuations and cycles; Econometric and statistical methods (search for similar items in EconPapers)
JEL-codes: C53 E32 E37 (search for similar items in EconPapers)
Pages: 42 pages
Date: 2015
New Economics Papers: this item is included in nep-ets, nep-for, nep-mac, nep-ore and nep-ure
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https://www.bankofcanada.ca/wp-content/uploads/2015/06/wp2015-24.pdf

Related works:
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 (2017)
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:bca:bocawp:15-24

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