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Macroeconomic forecasting and structural analysis through regularized reduced-rank regression

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Author Info

  • Emmanuela Bernardini

    ()
    (Banca d'Italia)

  • Gianluca Cubadda

    ()
    (University of Rome "Tor Vergata")

Abstract

This paper proposes a strategy to detect and impose reduced-rank restrictions in medium vector autoregressive models. In this framework, it is known that Canonical Correlation Analysis (CCA) does not perform well because inversions of large covariance matrices are required. We propose a method that combines the richness of reduced-rank regression with the simplicity of naive univariate forecasting methods. In particular, we suggest to use a proper shrinkage estimator of the autocovariance matrices that are involved in the computation of CCA, thus obtaining a method that is asymptotically equivalent to CCA, but it is numerically more stable in finite samples. Simulations and empirical applications document the merits of the proposed approach both in forecasting and in structural analysis.

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Bibliographic Info

Paper provided by Tor Vergata University, CEIS in its series CEIS Research Paper with number 289.

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Length: 25 pages
Date of creation: 03 Oct 2013
Date of revision: 03 Oct 2013
Handle: RePEc:rtv:ceisrp:289

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Postal: CEIS - Centre for Economic and International Studies - Faculty of Economics - University of Rome "Tor Vergata" - Via Columbia, 2 00133 Roma
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Related research

Keywords: Reduced rank regression; vector autoregressive models; shrinkage estimation; macroeconomic forecasting.;

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References

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  1. Sancetta, A., 2006. "Sample Covariance Shrinkage for High Dimensional Dependent Data," Cambridge Working Papers in Economics 0637, Faculty of Economics, University of Cambridge.
  2. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, Elsevier, vol. 13(2), pages 281-291, June.
  3. Carriero, Andrea & Kapetanios, George & Marcellino, Massimiliano, 2009. "Forecasting Large Datasets with Bayesian Reduced Rank Multivariate Models," CEPR Discussion Papers 7446, C.E.P.R. Discussion Papers.
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Cited by:
  1. Inske Pirschel & Maik Wolters, 2014. "Forecasting German Key Macroeconomic Variables Using Large Dataset Methods," Kiel Working Papers 1925, Kiel Institute for the World Economy.

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