Arbeitspapier
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 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.
- Sprache
-
Englisch
- Erschienen in
-
Series: Bank of Canada Working Paper ; No. 2015-24
- Klassifikation
-
Wirtschaft
Forecasting Models; Simulation Methods
Business Fluctuations; Cycles
Prices, Business Fluctuations, and Cycles: Forecasting and Simulation: Models and Applications
- Thema
-
Business fluctuations and cycles
Econometric and statistical methods
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Guérin, Pierre
Leiva-Leon, Danilo
- Ereignis
-
Veröffentlichung
- (wer)
-
Bank of Canada
- (wo)
-
Ottawa
- (wann)
-
2015
- DOI
-
doi:10.34989/swp-2015-24
- Handle
- Letzte Aktualisierung
- 10.03.2025, 11:42 MEZ
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Guérin, Pierre
- Leiva-Leon, Danilo
- Bank of Canada
Entstanden
- 2015
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