PRIOR SELECTION FOR VECTOR AUTOREGRESSIONS

• 成果类型: Article
• 署名作者: Giannone, Domenico; Lenza, Michele; Primiceri, Giorgio E.
• 署名单位: Luiss Guido Carli University; Universite Libre de Bruxelles; Centre for Economic Policy Research - UK; European Central Bank; Northwestern University; Centre for Economic Policy Research - UK; National Bureau of Economic Research
• 刊物名称: REVIEW OF ECONOMICS AND STATISTICS
• ISSN/ISSBN: 0034-6535
• DOI: 10.1162/REST_a_00483
• 发表日期: 2015-05
• 页码: 436-451
• 关键词: hierarchical shrinkage; monetary-policy; MODEL; inflation; forecasts; shocks

摘要

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 nave 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 forecastingas well as factor modelsand accuracy in the estimation of impulse response functions.