Marco Del Negro and Frank Schorfheide
Working Paper 2002-14a
Revised July 2003
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This paper uses a simple New-Keynesian dynamic stochastic general equilibrium model as a prior for a vector autoregression, shows that the resulting model is competitive with standard benchmarks in terms of forecasting, and can be used for policy analysis.
JEL classification: C11, C32, C53
Keywords: Bayesian analysis, DSGE models, forecasting, vector autoregressions
Part of this research was conducted while the second author was visiting the research department of the Federal Reserve Bank of Atlanta, for whose hospitality he is grateful. The second author gratefully acknowledges financial support from the University Research Foundation of the University of Pennsylvania. We thank two anonymous referees, Sean Campbell, Fabio Canova, Frank Diebold, John Geweke, Dave Gordon, Thomas Lubik, Jim Nason, Chris Sims, Dan Waggoner, Charles Whiteman, Tao Zha, and seminar participants at the 2003 ASSA Meetings, Bank of Canada, Brown University, 2002 CIRANO Forecasting Conference, Duke University, European Central Bank, 2002 European Econometric Society Meetings, Federal Reserve Bank of Atlanta, 2002 LACEA Meetings, Spring 2002 Macro Federal Reserve System Meetings, Indiana University, 2002 Midwest Macro Conference, 2002 SED Meetings, University of Pennsylvania, and UCSD for helpful comments and discussion. We would like to thank Iskander Karibzhanov for translating some of our Matlab code in C. The views expressed here are the authors’ and not necessarily those of the Federal Reserve Bank of Atlanta or the Federal Reserve System. Any remaining errors are the authors’ responsibility.
Please address questions regarding content to Marco Del Negro, Research Department, Federal Reserve Bank of Atlanta, 1000 Peachtree Street, N.E., Atlanta, Georgia 30309-4470, marco delnegro@atl.frb.org, 404-498-8561, or Frank Schorfheide, Department of Economics, University of Pennsylvania, 3718 Locust Walk, Philadelphia, Pennsylvania 19104-6297, schorf@ssc.upenn.edu, 215-898-8486.