Bayesian methods for dynamic multivariate models

If dynamic multivariate models are to be used to guide decision-making, it is important that probability assessments of forecasts or policy projections be provided. When identified Bayesian vector autoregression (VAR) models are presented with error bands in the existing literature, both conceptual and numerical problems have not been dealt with in an internally consistent way. In this paper we develop methods to introduce prior information in both reduced-form and structural VAR models without introducing substantial new computational burdens. Our approach makes it feasible to use a single, large dynamic framework (for example, 20-variable models) for tasks of policy projections.