Prior selection for vector autoregressions

Domenico Giannone, Michele Lenza and Giorgio Primiceri

No 1494, Working Paper Series from European Central Bank

Abstract: 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

Keywords: Bayesian methods; forecasting; hierarchical modeling; Impulse responses; marginal likelihood (search for similar items in EconPapers)
Date: 2012-11
New Economics Papers: this item is included in nep-ets and nep-for
Note: 411196
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Citations: View citations in EconPapers (85)

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Related works:
Journal Article: Prior Selection for Vector Autoregressions (2015)
Working Paper: Prior Selection for Vector Autoregressions (2012)
Working Paper: Prior Selection for Vector Autoregressions (2012)
Working Paper: Prior Selection for Vector Autoregressions (2012)
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Persistent link: https://EconPapers.repec.org/RePEc:ecb:ecbwps:20121494

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