Drawing Conclusions from Structural Vector Autoregressions Identified on the Basis of Sign Restrictions

Christiane Baumeister and James Hamilton

No 14271, CEPR Discussion Papers from Centre for Economic Policy Research

Abstract: This paper discusses the problems associated with using information about the signs of certain magnitudes as a basis for drawing structural conclusions in vector autoregressions. We also review available tools to solve these problems. For illustration we use Dahlhaus and Vasishtha’s (2019) study of the effects of a U.S. monetary contraction on capital flows to emerging markets. We explain why sign restrictions alone are not enough to allow us to answer the question and suggest alternative approaches that could be used.

Keywords: Structural vector autoregressions; Sign restrictions; Identified set; Informative priors; Bayesian inference; Monetary policy; Capital flows (search for similar items in EconPapers)
JEL-codes: C30 E5 F2 (search for similar items in EconPapers)
Date: 2020-01
New Economics Papers: this item is included in nep-mac
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (44)

Downloads: (external link)
https://cepr.org/publications/DP14271 (application/pdf)

Related works:
Journal Article: Drawing conclusions from structural vector autoregressions identified on the basis of sign restrictions (2020)
Working Paper: Drawing Conclusions from Structural Vector Autoregressions Identified on the Basis of Sign Restrictions (2020)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:cpr:ceprdp:14271

Ordering information: This working paper can be ordered from
https://cepr.org/publications/DP14271

Access Statistics for this paper

More papers in CEPR Discussion Papers from Centre for Economic Policy Research 33 Great Sutton Street, London EC1V 0DX, UK.
Bibliographic data for series maintained by CEPR ().