LARGE BAYESIAN VECTOR AUTO REGRESSIONS
LARGE BAYESIAN VECTOR AUTO REGRESSIONS
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DOI：
10.1002/jae.1137
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发表时间：
2010-01-01
影响因子：
2.1
通讯作者：
Reichlin, Lucrezia
中科院分区：
文献类型：
--
作者：
Banbura, Marta;Giannone, Domenico;Reichlin, Lucrezia
本文表明，具有贝叶斯收缩的向量自回归（VAR）是大型动态模型的合适工具。我们以 De Mol 和同事 (2008) 的结果为基础，表明当收缩程度与横截面维度相关时，小货币 VAR 的预测性能可以通过添加额外的宏观经济变量和部门信息来提高。此外，我们还表明，具有收缩的大型 VARS 会产生可靠的脉冲响应，并且适合结构分析。 (C) 2009 年约翰·威利父子公司。有限公司
This paper shows that vector auto regression (VAR) with Bayesian shrinkage is an appropriate tool for large dynamics models. We build on the results of De Mol and co-workers (2008) and show that, when the degree of shrinkage is set in relation to the cross-sectional dimension, the forecasting performance of small monetary VARs can be improved by adding additional macroeconomic variables and sectoral information. In addition, we show that large VARS with shrinkage produce credible impulse responses and are suitable for structural analysis. (C) 2009 John Wiley & Sons. Ltd