Arbeitspapier
Forecasting large datasets with reduced rank multivariate models
The paper addresses the issue of forecasting a large set of variables using multivariate models. In particular, we propose three alternative reduced rank forecasting models and compare their predictive performance with the most promising existing alternatives, namely, factor models, large scale bayesian VARs, and multivariate boosting. Specifically, we focus on classical reduced rank regression, a two-step procedure that applies, in turn, shrinkage and reduced rank restrictions, and the reduced rank bayesian VAR of Geweke (1996). As a result, we found that using shrinkage and rank reduction in combination rather than separately improves substantially the accuracy of forecasts, both when the whole set of variables is to be forecast, and for key variables such as industrial production growth, inflation, and the federal funds rate.
- Language
-
Englisch
- Bibliographic citation
-
Series: Working Paper ; No. 617
- Classification
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Wirtschaft
Bayesian Analysis: General
Estimation: General
Multiple or Simultaneous Equation Models: Panel Data Models; Spatio-temporal Models
Forecasting Models; Simulation Methods
- Subject
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Bayesian VARs
Factor models
Forecasting
Reduced rank
Multivariate Analyse
Prognoseverfahren
Ranking-Verfahren
Bayes-Statistik
- Event
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Geistige Schöpfung
- (who)
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Carriero, Andrea
Kapetanios, George
Marcellino, Massimiliano
- Event
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Veröffentlichung
- (who)
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Queen Mary University of London, Department of Economics
- (where)
-
London
- (when)
-
2007
- Handle
- Last update
-
10.03.2025, 11:42 AM CET
Data provider
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ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Carriero, Andrea
- Kapetanios, George
- Marcellino, Massimiliano
- Queen Mary University of London, Department of Economics
Time of origin
- 2007
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