Abstract
The literature on graphical models and the literature on identification pursue similar goals, but do not use entirely each other's results, because represent them in different languages. To ease the communication between these fields, I translate the most important theorems on identification of linear Gaussian Simultaneous Equations Models (SEMs) and Structural Vector Autoregressions (SVARs) into the language of graphical models. I propose graphical interpretations of the rank conditions for identification of SEMs, of the rank condition of Rubio-Ramirez et al (2010) for identification of SVARs with linear and nonlinear restrictions, and of the theory of partial identification for SVARs.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
