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Priors for the long run

Author

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

Abstract

We propose a class of prior distributions that discipline the long-run behavior of Vector Autoregressions (VARs). These priors can be naturally elicited using economic theory, which provides guidance on the joint dynamics of macroeconomic time series in the long run. Our priors for the long run are conjugate, and can thus be easily implemented using dummy observations and combined with other popular priors. In VARs with standard macroeconomic variables, a prior based on the long-run predictions of a wide class of theoretical models yields substantial improvements in the forecasting performance. JEL Classification: C11, C32, E37

Suggested Citation

  • Giannone, Domenico & Lenza, Michele & Primiceri, Giorgio E., 2018. "Priors for the long run," Working Paper Series 2132, European Central Bank.
    • Handle: RePEc:ecb:ecbwps:20182132
    Note: 411196
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    References listed on IDEAS

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    More about this item

    Keywords

    Bayesian vector autoregression; forecasting; hierarchical model; initial conditions; overfitting;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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