Bayesian methods for dynamic multivariate models
If multivariate dynamic models are to be used to guide decision-making, it is important that it be possible to provide probability assessments of their results. Bayesian VAR models in the existing literature have not commonly (in fact, not at all as far as we know) been presented with error bands around forecasts or policy projections based on the posterior distribution. In this paper we show that it is possible to introduce prior information in both reduced form and structural VAR models without introducing substantial new computational burdens. With our approach, identified VAR analysis of large systems (e.g., 20-variable models) becomes possible.
|Date of creation:||1996|
|Date of revision:|
|Contact details of provider:|| Postal: |
Web page: http://www.frbatlanta.org/Email:
More information through EDIRC
|Order Information:|| Email: |
When requesting a correction, please mention this item's handle: RePEc:fip:fedawp:96-13. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Meredith Rector)
If references are entirely missing, you can add them using this form.