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Prior selection for vector autoregressions

Author

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  • Giannone, Domenico
  • Lenza, Michele
  • Primiceri, Giorgio E.

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

Suggested Citation

  • Giannone, Domenico & Lenza, Michele & Primiceri, Giorgio E., 2012. "Prior selection for vector autoregressions," Working Paper Series 1494, European Central Bank.
    • Handle: RePEc:ecb:ecbwps:20121494
    Note: 411196
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    References listed on IDEAS

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    More about this item

    Keywords

    Bayesian methods; forecasting; hierarchical modeling; Impulse responses; marginal likelihood;
    All these keywords.

    JEL classification:

    • E00 - Macroeconomics and Monetary Economics - - General - - - General
    • E30 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - General (includes Measurement and Data)

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