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

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).