Forecasting Large Datasets with Bayesian Reduced Rank Multivariate Models
Andrea Carriero (),
George Kapetanios and
No 7446, CEPR Discussion Papers from C.E.P.R. Discussion Papers
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 for US time series 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). We find 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. The robustness of this finding is confirmed by a Monte Carlo experiment based on bootstrapped data. We also provide a consistency result for the reduced rank regression valid when the dimension of the system tends to infinity, which opens the ground to use large scale reduced rank models for empirical analysis.
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)
New Economics Papers: this item is included in nep-cba, nep-ecm and nep-for
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Journal Article: Forecasting large datasets with Bayesian reduced rank multivariate models (2011)
Working Paper: Forecasting Large Datasets with Bayesian Reduced Rank Multivariate Models (2009)
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