Forecasting Large Datasets with Bayesian Reduced Rank Multivariate Models

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Show simple item record CARRIERO, Andrea KAPETANIOS, George MARCELLINO, Massimiliano 2012-01-18T09:16:47Z 2012-01-18T09:16:47Z 2011
dc.identifier.citation Journal of Applied Econometrics, 2011, 26, 5, 736-761 en
dc.identifier.issn 0883-7252
dc.description (Published version of EUI ECO Working Paper 2009/31.) en
dc.description.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 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 way to using large-scale reduced rank models for empirical analysis. en
dc.language.iso en en
dc.relation.ispartof Journal of Applied Econometrics en
dc.title Forecasting Large Datasets with Bayesian Reduced Rank Multivariate Models en
dc.type Article en
dc.identifier.doi 10.1002/jae.1150
dc.neeo.contributor CARRIERO|Andrea|aut|
dc.neeo.contributor KAPETANIOS|George|aut|
dc.neeo.contributor MARCELLINO|Massimiliano|aut|EUI70008
dc.identifier.volume 26 en

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