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Large Bayesian VARs

Author

Listed:
  • Giannone, Domenico
  • Reichlin, Lucrezia
  • Bańbura, Marta

Abstract

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. JEL Classification: C11, C13, C33, C53

Suggested Citation

  • Giannone, Domenico & Reichlin, Lucrezia & Bańbura, Marta, 2008. "Large Bayesian VARs," Working Paper Series 966, European Central Bank.
    • Handle: RePEc:ecb:ecbwps:2008966
    Note: 93468
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    References listed on IDEAS

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    More about this item

    Keywords

    Bayesian VAR; forecasting; large cross-sections; monetary VAR;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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