Priors for the long run

Domenico Giannone, Michele Lenza and Giorgio Primiceri

No 2132, Working Paper Series from European Central Bank

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

Keywords: Bayesian vector autoregression; forecasting; hierarchical model; initial conditions; overfitting (search for similar items in EconPapers)
Date: 2018-02
Note: 411196
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (13)

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Related works:
Journal Article: Priors for the Long Run (2019)
Working Paper: Priors for the long run (2017)
Working Paper: Priors for the Long Run (2016)
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Persistent link: https://EconPapers.repec.org/RePEc:ecb:ecbwps:20182132

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