A0355
Title: Flexible priors and restrictions for structural vector autoregressions
Authors: Francesca Loria - Federal Reserve Board (United States) [presenting]
Christiane Baumeister - University of Notre Dame (United States)
Junior Maih - Norges Bank and Norwegian school of management (BI) (Norway)
Abstract: The purpose is to introduce an innovative approach for estimating Bayesian vector autoregressions (VAR) in structural form, enhancing flexibility in incorporating various priors and identification strategies. The method accommodates zero, sign, and narrative restrictions, as well as identification via proxy variables, offering a unified framework for replicating prominent strategies in the VAR literature. Unlike existing methods, it directly elicits informative priors and restrictions on structural parameters, ensuring transparency and avoiding unintended beliefs. The approach is versatile, scalable to larger models, and eliminates the need for separate algorithms for different identification schemes. Additionally, the methodology allows for imposing (in-)equality restrictions on VAR parameters, providing a robust means to incorporate strong beliefs. Overall, this user-friendly framework addresses key challenges in the current literature, offering a valuable tool for empirical researchers, with the method accessible through the RISE toolbox.
