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Forecasting Large Datasets with Bayesian Reduced Rank Multivariate Models

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  • Andrea Carriero
  • George Kapetanios
  • Massimiliano Marcellino

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. Speci.cally, 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 .nd 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.

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Bibliographic Info

Paper provided by European University Institute in its series Economics Working Papers with number ECO2009/31.

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Date of creation: 2009
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Handle: RePEc:eui:euiwps:eco2009/31

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Keywords: Bayesian VARs; factor models; forecasting; reduced rank;

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References

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Citations

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Cited by:
  1. Karlsson, Sune, 2012. "Forecasting with Bayesian Vector Autoregressions," Working Papers 2012:12, Örebro University, School of Business.
  2. Marco J. Lombardi & Philipp Maier, 2011. "Forecasting economic growth in the euro area during the great moderation and the great recession," Working Paper Series 1379, European Central Bank.
  3. Carriero, Andrea & Clark, Todd & Marcellino, Massimiliano, 2012. "Common Drifting Volatility in Large Bayesian VARs," CEPR Discussion Papers 8894, C.E.P.R. Discussion Papers.
  4. Carriero, Andrea & Kapetanios, George & Marcellino, Massimiliano, 2010. "Forecasting Government Bond Yields with Large Bayesian VARs," CEPR Discussion Papers 7796, C.E.P.R. Discussion Papers.
  5. Christophe Croux & Peter Exterkate, 2011. "Sparse and Robust Factor Modelling," Tinbergen Institute Discussion Papers 11-122/4, Tinbergen Institute.
  6. Teresa Buchen & Klaus Wohlrabe, 2013. "Assessing the Macroeconomic Forecasting Performance of Boosting - Evidence for the United States, the Euro Area, and Germany," CESifo Working Paper Series 4148, CESifo Group Munich.
  7. Andrea Carriero & Todd Clark & Massimiliano Marcellino, 2011. "Bayesian VARs: specification choices and forecast accuracy," Working Paper 1112, Federal Reserve Bank of Cleveland.
  8. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2012. "Real-time nowcasting with a Bayesian mixed frequency model with stochastic volatility," Working Paper 1227, Federal Reserve Bank of Cleveland.
  9. Croux, Christophe & Exterkate, Peter, 2011. "Robust and sparse factor modelling," Open Access publications from Katholieke Universiteit Leuven urn:hdl:123456789/314742, Katholieke Universiteit Leuven.

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