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Large Bayesian VARs

  • Martha Banbura
  • Domenico Giannone
  • Lucrezia Reichlin

This paper shows that Vector Autoregression with Bayesian shrinkage is an appropriate tool for large dynamic models. We build on the results by De Mol, Giannone, and Reichlin (2008) and show that, when the degree of shrinkage is set in relation to the cross-sectional dimension, the forecasting performance of small monetary VARs can be improved by adding additional macroeconomic variables and sectoral information. In addition, we show that large VARs with shrinkage produce credible impulse responses and are suitable for structural analysis.

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File URL: https://dipot.ulb.ac.be/dspace/bitstream/2013/54095/1/RePEc_eca_wpaper_2008_033.pdf
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Paper provided by ULB -- Universite Libre de Bruxelles in its series Working Papers ECARES with number 2008_033.

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Length: 37 p.
Date of creation: 2008
Date of revision:
Publication status: Published by:
Handle: RePEc:eca:wpaper:2008_033
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  3. Mario Forni & Domenico Giannone & Marco Lippi & Lucrezia Reichlin, 2008. "Opening the Black Box: Structural Factor Models with Large Cross-Sections," Working Papers ECARES 2008_036, ULB -- Universite Libre de Bruxelles.
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