Priors for the Long Run

Domenico Giannone, Michele Lenza, Giorgio E. Primiceri*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

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. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

Original languageEnglish (US)
Pages (from-to)565-580
Number of pages16
JournalJournal of the American Statistical Association
Volume114
Issue number526
DOIs
StatePublished - Apr 3 2019

Keywords

  • Bayesian vector autoregression
  • Forecasting
  • Hierarchical model
  • Initial conditions
  • Overfitting

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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