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Forecasting Large Datasets with Reduced Rank Multivariate Models

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Author Info

  • Andrea Carriero

    ()
    (Queen Mary, University of London)

  • George Kapetanios

    ()
    (Queen Mary, University of London)

  • Massimiliano Marcellino

    ()
    (IEP-Bocconi University, IGIER and CEPR)

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.

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Bibliographic Info

Paper provided by Queen Mary, University of London, School of Economics and Finance in its series Working Papers with number 617.

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Date of creation: Oct 2007
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Handle: RePEc:qmw:qmwecw:wp617

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Keywords: Bayesian VARs; Factor models; Forecasting; Reduced rank;

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References

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  1. Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia, 1999. "The Generalized Dynamic Factor Model: Identification and Estimation," CEPR Discussion Papers 2338, C.E.P.R. Discussion Papers.
  2. Kadiyala, K. Rao & Karlsson, Sune, 1994. "Numerical Aspects of Bayesian VAR-modeling," Working Paper Series in Economics and Finance 12, Stockholm School of Economics.
  3. Camba-Mendez, Gonzalo, et al, 2003. "Tests of Rank in Reduced Rank Regression Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 21(1), pages 145-55, January.
  4. Ziegler, Christina & Eickmeier, Sandra, 2006. "How good are dynamic factor models at forecasting output and inflation? A meta-analytic approach," Discussion Paper Series 1: Economic Studies 2006,42, Deutsche Bundesbank, Research Centre.
  5. Geweke, John, 1996. "Bayesian reduced rank regression in econometrics," Journal of Econometrics, Elsevier, vol. 75(1), pages 121-146, November.
  6. n/a, 2001. "A Comparison of Personal Sector Saving Rates in the UK, US and Italy," NIESR Discussion Papers 150, National Institute of Economic and Social Research.
  7. Inoue, Atsushi & Kilian, Lutz, 2004. "Bagging Time Series Models," CEPR Discussion Papers 4333, C.E.P.R. Discussion Papers.
  8. Thomas Doan & Robert B. Litterman & Christopher A. Sims, 1986. "Forecasting and conditional projection using realistic prior distribution," Staff Report 93, Federal Reserve Bank of Minneapolis.
  9. Breitung, Jörg & Eickmeier, Sandra, 2005. "Dynamic factor models," Discussion Paper Series 1: Economic Studies 2005,38, Deutsche Bundesbank, Research Centre.
  10. Stock, James H. & Watson, Mark W., 2006. "Forecasting with Many Predictors," Handbook of Economic Forecasting, Elsevier.
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Citations

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Cited by:
  1. Jana Eklund & George Kapetanios, 2008. "A Review of Forecasting Techniques for Large Data Sets," National Institute Economic Review, National Institute of Economic and Social Research, vol. 203(1), pages 109-115, January.
  2. Branimir Jovanovic & Magdalena Petrovska, 2010. "Forecasting Macedonian GDP: Evaluation of different models for short-term forecasting," Working Papers 2010-02, National Bank of the Republic of Macedonia, revised Aug 2010.
  3. Branimir, Jovanovic & Magdalena, Petrovska, 2010. "Forecasting Macedonian GDP: Evaluation of different models for short-term forecasting," MPRA Paper 43162, University Library of Munich, Germany.

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