Forecasting Large Datasets with Reduced Rank Multivariate Models
Andrea Carriero (),
George Kapetanios and
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George Kapetanios: Queen Mary, University of London
No 617, Working Papers from Queen Mary University of London, School of Economics and Finance
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.
Keywords: Bayesian VARs; Factor models; Forecasting; Reduced rank (search for similar items in EconPapers)
JEL-codes: C11 C13 C33 C53 (search for similar items in EconPapers)
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Working Paper: Forecasting Large Datasets with Reduced Rank Multivariate Models (2007)
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Persistent link: https://EconPapers.repec.org/RePEc:qmw:qmwecw:617
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