Priors for the Long Run

B-Tier
Journal: Journal of the American Statistical Association
Year: 2019
Volume: 114
Issue: 526
Pages: 565-580

Score contribution per author:

0.670 = (α=2.01 / 3 authors) × 1.0x B-tier

α: calibrated so average coauthorship-adjusted count equals average raw count

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.

Technical Details

RePEc Handle
repec:taf:jnlasa:v:114:y:2019:i:526:p:565-580
Journal Field
Econometrics
Author Count
3
Added to Database
2026-01-25