Bayesian Methods for Dynamic Multivariate Models

FRB Atlanta Working Paper No. 96-13

Posted: 4 Feb 1997

See all articles by Christopher A. Sims

Christopher A. Sims

Princeton University - Department of Economics; National Bureau of Economic Research (NBER)

Tao A. Zha

Federal Reserve Bank of Atlanta; Emory University

Date Written: Undated

Abstract

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.

JEL Classification: C11, C53

Suggested Citation

Sims, Christopher A. and Zha, Tao A., Bayesian Methods for Dynamic Multivariate Models (Undated). FRB Atlanta Working Paper No. 96-13, Available at SSRN: https://ssrn.com/abstract=4608

Christopher A. Sims

Princeton University - Department of Economics ( email )

Princeton, NJ 08544-1021
United States

National Bureau of Economic Research (NBER)

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

Tao A. Zha (Contact Author)

Federal Reserve Bank of Atlanta ( email )

1000 Peachtree Street N.E.
Atlanta, GA 30309-4470
United States
404-521-8353 (Phone)
404-521-8956 (Fax)

Emory University ( email )

201 Dowman Drive
Atlanta, GA 30322
United States

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