Prior Parameter Uncertainty: Some Implications for Forecasting and Policy Analysis with VAR Models

Federal Reserve Bank of Atlanta, Research Department Working Paper No. 99-13

Posted: 27 Oct 1999

See all articles by John Robertson

John Robertson

Federal Reserve Bank of Atlanta

Ellis W. Tallman

Federal Reserve Bank of Cleveland

Date Written: October 1999

Abstract

Models used for policy analysis should generate reliable unconditional forecasts as well as policy simulations (conditional forecasts) that are based on a structural model of the economy. Vector autoregression (VAR) models have been criticized for having inaccurate forecasts as well as being difficult to interpret in the context of an underlying economic model. In this paper, we examine how the treatment of prior uncertainty about parameter values can affect forecasting accuracy and the interpretation of identified structural VAR models.

Typically, VAR models are specified with long lag orders and a diffuse prior about the unrestricted coefficients. We find evidence that alternatives that emphasize nonstationary aspects of the data as well as parsimony in parameterization have better out-of-sample forecast performance and smoother and more persistent responses to a given exogenous monetary policy change than do unrestricted VARs.

JEL Classification: E44, C53

Suggested Citation

Robertson, John C. and Tallman, Ellis W., Prior Parameter Uncertainty: Some Implications for Forecasting and Policy Analysis with VAR Models (October 1999). Federal Reserve Bank of Atlanta, Research Department Working Paper No. 99-13, Available at SSRN: https://ssrn.com/abstract=188809

John C. Robertson

Federal Reserve Bank of Atlanta ( email )

1000 Peachtree Street, NE
Atlanta, GA 30309-4470
United States
404-521-8782 (Phone)
404-521-8956 (Fax)

Ellis W. Tallman (Contact Author)

Federal Reserve Bank of Cleveland ( email )

East 6th & Superior
Cleveland, OH 44101-1387
United States

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