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Take your model bowling: forecasting with general equilibrium models

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  • Marco Del Negro
  • Frank Schorfheide

Abstract

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.

Suggested Citation

  • Marco Del Negro & Frank Schorfheide, 2003. "Take your model bowling: forecasting with general equilibrium models," Economic Review, Federal Reserve Bank of Atlanta, issue Q4, pages 35-50.
  • Handle: RePEc:fip:fedaer:y:2003:i:q4:p:35-50:n:v.88no.4
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    Cited by:

    1. Dan S. Rickman, 2010. "Modern Macroeconomics And Regional Economic Modeling," Journal of Regional Science, Wiley Blackwell, vol. 50(1), pages 23-41.
    2. Kirdan Lees & Troy Matheson & Christie Smith, 2007. "Open Economy Dsge-Var Forecasting And Policy Analysis: Head To Head With The Rbnz Published Forecasts," CAMA Working Papers 2007-05, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    3. Lees, Kirdan & Matheson, Troy & Smith, Christie, 2011. "Open economy forecasting with a DSGE-VAR: Head to head with the RBNZ published forecasts," International Journal of Forecasting, Elsevier, vol. 27(2), pages 512-528, April.
    4. Tovar, Camilo Ernesto, 2009. "DSGE Models and Central Banks," Economics - The Open-Access, Open-Assessment E-Journal, Kiel Institute for the World Economy (IfW), vol. 3, pages 1-31.
    5. Muhanji, Stella & Ojah, Kalu, 2011. "External shocks and persistence of external debt in open vulnerable economies: The case of Africa," Economic Modelling, Elsevier, vol. 28(4), pages 1615-1628, July.
    6. Javier Andrés & Fernando Restoy, 2007. "Macroeconomic modelling in EMU: how relevant is the change in regime?," Working Papers 0718, Banco de España;Working Papers Homepage.
    7. Muhanji, Stella & Malikane, Christopher & Ojah, Kalu, 2013. "Price and liquidity puzzles of a monetary shock: Evidence from indebted African economies," Economic Modelling, Elsevier, vol. 33(C), pages 620-630.
    8. Alvarez-Lois, Pedro & Harrison, Richard & Piscitelli, Laura & Scott, Alasdair, 2008. "On the application and use of DSGE models," Journal of Economic Dynamics and Control, Elsevier, vol. 32(8), pages 2428-2452, August.

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