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

Pierre Guérin and Danilo Leiva-Leon ()

Economics Letters, 2017, vol. 157, issue C, 45-49

Abstract: 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.

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)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0165176517302057
Full text for ScienceDirect subscribers only

Related works:
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 (2015)
Working Paper: Model Averaging in Markov-Switching Models: Predicting National Recessions with Regional Data (2014)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:ecolet:v:157:y:2017:i:c:p:45-49

DOI: 10.1016/j.econlet.2017.05.027

Access Statistics for this article

Economics Letters is currently edited by Economics Letters Editorial Office

More articles in Economics Letters from Elsevier
Bibliographic data for series maintained by Catherine Liu ().