Priors for the Long Run

• 成果类型: Article
• 署名作者: Giannone, Domenico; Lenza, Michele; Primiceri, Giorgio E.
• 署名单位: Federal Reserve System - USA; Federal Reserve Bank - New York; Centre for Economic Policy Research - UK; European Central Bank; Northwestern University; National Bureau of Economic Research
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2018.1483826
• 发表日期: 2019
• 页码: 565-580
• 关键词: bayesian-analysis; error-correction; Unit roots; cointegration; inflation; posterior

摘要

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.