Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/62799
Authors: 
Carriero, Andrea
Kapetanios, George
Marcellino, Massimiliano
Year of Publication: 
2007
Series/Report no.: 
Working Paper, Department of Economics, Queen Mary, University of London 617
Abstract: 
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.
Subjects: 
Bayesian VARs
Factor models
Forecasting
Reduced rank
JEL: 
C11
C13
C33
C53
Document Type: 
Working Paper

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