Exact identification, robust inference, and shock masquerading in sign-restricted SVARs

Hyeon-seung Huh and David Kim
Additional contact information
Hyeon-seung Huh: Yonsei University
David Kim: University of Sydney

No 2026rwp-293, Working papers from Yonsei University, Yonsei Economics Research Institute

Abstract: We propose an alternative sampling scheme for sign-restricted SVARs that addresses two fundamental challenges in the standard approach: prior dependence and shock masquerading. The key idea is to utilize a direct sampling of structural coefficients in the SVAR combined with the robust inference of Giacomini and Kitagawa (2021). The scheme delivers large sets of exactly identified models, and the rotation matrix is uniquely solved via a direct, non-iterative linear algorithm. Sign restrictions are then used as a post-identification filter to ensure economic plausibility. This design is capable of eliminating prior dependence concerns, tightening the credible bounds, mitigating shock masquerading, and improving computational efficiency. We demonstrate the practical utility of the alternative sampling scheme through an application to the U.S. SVAR of Peersman (2005).

Keywords: Structural vector autoregressions; Exact identification; Sign restrictions; Haar prior; Robust inference; Shock masquerading; Givens matrix (search for similar items in EconPapers)
JEL-codes: C32 C36 C51 E32 E52 (search for similar items in EconPapers)
Pages: 35pages
Date: 2026-06
References: Add references at CitEc
Citations:

Downloads: (external link)
http://121.254.254.220/repec/yon/wpaper/2026rwp-293.pdf (application/pdf)

Related works:
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:yon:wpaper:2026rwp-293

Access Statistics for this paper

More papers in Working papers from Yonsei University, Yonsei Economics Research Institute Contact information at EDIRC.
Bibliographic data for series maintained by YERI ().