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Take your model bowling: forecasting with general equilibrium models

  • Marco Del Negro
  • Frank Schorfheide

During the past two decades, dynamic stochastic general equilibrium (DSGE) models have taken center stage in academic macroeconomics. Nonetheless, these models are still rarely used in policy-making and forecasting. ; This article describes the workings of the DSGE-VAR, a procedure that combines DSGE models and vector autoregressions (VARs). The procedure uses DSGE models as priors to restrict the VAR’s parameters. Since the VAR’s parameters are imprecisely estimated unless a very long time series of data is available, using DSGE priors can improve the VAR’s forecasting performance. Moreover, the Lucas critique implies that DSGE priors can be particularly useful when forecasting the impact of policy changes. ; The authors assess DSGE-VAR’s forecasting performance in terms of three variables that most interest monetary policymakers: real output growth, inflation, and the federal funds rate. Their results show that the DSGE-VAR forecast is superior to that of unrestricted VARs and comparable to that of VARs with Minnesota priors. ; The article also discusses how DSGE-VAR can be used to identify the fundamental shocks that hit the economy and to forecast the impact of changes in the policy rule followed by the monetary authorities. ; Perhaps in the not-too-distant future, practitioners and policymakers will be able to use a full-fledged DSGE model for both forecasting and policy assessment. In the meantime, the authors argue, DSGE-VAR may provide a viable alternative to the models currently used.

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Article provided by Federal Reserve Bank of Atlanta in its journal Economic Review.

Volume (Year): (2003)
Issue (Month): Q4 ()
Pages: 35-50

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Handle: RePEc:fip:fedaer:y:2003:i:q4:p:35-50:n:v.88no.4
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  1. Lawrence J. Christiano & Martin Eichenbaum & Charles Evans, 2001. "Nominal rigidities and the dynamic effects of a shock to monetary policy," Proceedings, Federal Reserve Bank of San Francisco, issue Jun.
  2. Ray C. Fair & Arnold Zellner (ary), 1992. "The Cowles Commission approach, real business cycles theories, and New- Keynesian economics," Proceedings, Federal Reserve Bank of St. Louis, pages 133-157.
  3. Thomas Doan & Robert B. Litterman & Christopher A. Sims, 1986. "Forecasting and conditional projection using realistic prior distribution," Staff Report 93, Federal Reserve Bank of Minneapolis.
  4. Brayton, Flint & Levin, Andrew & Lyon, Ralph & Williams, John C., 1997. "The evolution of macro models at the Federal Reserve Board," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 47(1), pages 43-81, December.
  5. Altug, Sumru, 1989. "Time-to-Build and Aggregate Fluctuations: Some New Evidence," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 30(4), pages 889-920, November.
  6. Ben S. Bernanke, 1986. "Alternative Explanations of the Money-Income Correlation," NBER Working Papers 1842, National Bureau of Economic Research, Inc.
  7. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
  8. Marco Del Negro & Frank Schorfheide, 2004. "Priors from General Equilibrium Models for VARS," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 45(2), pages 643-673, 05.
  9. Flint Brayton & Eileen Mauskopf & David Reifschneider & Peter Tinsley & John Williams, 1997. "The role of expectations in the FRB/US macroeconomic model," Federal Reserve Bulletin, Board of Governors of the Federal Reserve System (U.S.), issue Apr, pages 227-245.
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