Sign Restrictions, Structural Vector Autoregressions, and Useful Prior Information
收藏NBER2014-12-01 更新2025-01-04 收录
下载链接：
https://www.nber.org/papers/w20741
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资源简介：
This paper makes the following original contributions to the literature. (1) We develop a simpler analytical characterization and numerical algorithm for Bayesian inference in structural vector autoregressions that can be used for models that are overidentified, just-identified, or underidentified.
提供机构：
美国国家经济研究局
创建时间：
2014-12-01
