Prior selection for vector autoregressions

Domenico Giannone, Michele Lenza, Giorgio E. Primiceri

Research output: Contribution to journalArticlepeer-review

129 Scopus citations

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-of-sample 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 toward a parsimonious naive benchmark, and thus reduce estimation uncertainty. This paper studies the optimal choice of the informativeness of these priors, which we treat as additional parameters, in the spirit of hierarchical modeling. This approach, theoretically grounded and easy to implement, greatly reduces the number and importance of subjective choices in the setting of the prior. Moreover, it performs very well in terms of both out-of-sample forecasting-as well as factor models-and accuracy in the estimation of impulse response functions.

Original languageEnglish (US)
Pages (from-to)436-451
Number of pages16
JournalReview of Economics and Statistics
Volume97
Issue number2
DOIs
StatePublished - May 1 2015

ASJC Scopus subject areas

  • Social Sciences (miscellaneous)
  • Economics and Econometrics

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