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Prior selection for vector autoregressions

Listed author(s):
  • Giannone, Domenico
  • Lenza, Michele
  • Primiceri, Giorgio E.

Vector autoregressions (VARs) are flexible time series models that can capture complex dynamic interrelationships among macroeconomic variables. However, their dense parameterization leads to unstable inference and inaccurate out-ofsample forecasts, particularly for models with many variables. A solution to this problem is to use informative priors, in order to shrink the richly parameterized unrestricted model towards a parsimonious na JEL Classification: C11, C32, C53, E37

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Paper provided by European Central Bank in its series Working Paper Series with number 1494.

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Date of creation: Nov 2012
Handle: RePEc:ecb:ecbwps:20121494
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  28. De Mol, Christine & Giannone, Domenico & Reichlin, Lucrezia, 2008. "Forecasting using a large number of predictors: Is Bayesian shrinkage a valid alternative to principal components?," Journal of Econometrics, Elsevier, vol. 146(2), pages 318-328, October.
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