Conditional Forecasts in Dynamic Multivariate Models

43 Pages Posted: 25 Jan 2015

See all articles by Daniel F. Waggoner

Daniel F. Waggoner

Federal Reserve Bank of Atlanta

Tao A. Zha

Federal Reserve Bank of Atlanta; Emory University

Date Written: December 1998

Abstract

In the existing literature, conditional forecasts in the vector autoregressive (VAR) framework have not been commonly presented with probability distributions or error bands. This paper develops Bayesian methods for computing such distributions or bands. It broadens the class of conditional forecasts to which the methods can be applied. The methods work for both structural and reduced-form VAR models and, in contrast to common practices, account for the parameter uncertainty in small samples. Empirical examples under the flat prior and under the reference prior of Sims and Zha (1998) are provided to show the use of these methods.

Keywords: conditional forecasts, hard and soft conditions, Bayesian methods, probability distribution, error bands, likelihood

JEL Classification: C32, E17, C53

Suggested Citation

Waggoner, Daniel F. and Zha, Tao A., Conditional Forecasts in Dynamic Multivariate Models (December 1998). FRB Atlanta Working Paper Series No. 98-22, Available at SSRN: https://ssrn.com/abstract=2511338 or http://dx.doi.org/10.2139/ssrn.2511338

Daniel F. Waggoner

Federal Reserve Bank of Atlanta ( email )

1000 Peachtree Street N.E.
Atlanta, GA 30309-4470
United States
404-521-8278 (Phone)

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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