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Forecasting under Model Uncertainty

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  • Wolters, Maik H.

Abstract

This paper investigates the accuracy of point and density forecasts of four dynamic stochastic general equilibrium (DSGE) models for output growth, inflation and the interest rate. The model parameters are estimated and forecasts are derived successively from historical U.S. data vintages synchronized with the Fed’s Greenbook projections. In addition, I compute weighted forecasts using simple combination schemes as well as likelihood based methods. While forecasts from structuralmodels fail to forecast large recessions and booms, they are quite accurate during normal times. Model forecasts compare particularly well to nonstructural forecasts and to Greenbook projections for horizons of three quarters ahead and higher. Weighted forecasts are more precise than forecasts from single models. A simple average of forecasts yields an accuracy comparable to the one obtained with state of the art time series methods that can incorporate large datasets. Comparing density forecasts of DSGE models with the actual distribution of observations shows that the models overestimate uncertainty around point forecasts. --

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Bibliographic Info

Paper provided by Verein für Socialpolitik / German Economic Association in its series Annual Conference 2011 (Frankfurt, Main): The Order of the World Economy - Lessons from the Crisis with number 48723.

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Date of creation: 2011
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Handle: RePEc:zbw:vfsc11:48723

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Keywords: DSGE; forecasting; model uncertainty; density forecasts; business cycle models;

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Cited by:
  1. Marco Del Negro & Frank Schorfheide, 2012. "DSGE model-based forecasting," Staff Reports, Federal Reserve Bank of New York 554, Federal Reserve Bank of New York.
  2. Barbara Rossi & Tatevik Sekhposyan, 2014. "Alternative tests for correct specification of conditional predictive densities," Economics Working Papers, Department of Economics and Business, Universitat Pompeu Fabra 1416, Department of Economics and Business, Universitat Pompeu Fabra.
  3. Edward Herbst & Frank Schorfheide, 2011. "Evaluating DSGE model forecasts of comovements," Working Papers 11-5, Federal Reserve Bank of Philadelphia.
  4. Michael Wickens, 2014. "How Useful are DSGE Macroeconomic Models for Forecasting?," Open Economies Review, Springer, Springer, vol. 25(1), pages 171-193, February.
  5. Inske Pirschel & Maik Wolters, 2014. "Forecasting German Key Macroeconomic Variables Using Large Dataset Methods," Kiel Working Papers 1925, Kiel Institute for the World Economy.
  6. Michael Dotsey, 2013. "DSGE models and their use in monetary policy," Business Review, Federal Reserve Bank of Philadelphia, Federal Reserve Bank of Philadelphia, issue Q2, pages 10-16.
  7. Kolasa, Marcin & Rubaszek, Michał, 2014. "Forecasting with DSGE models with financial frictions," Dynare Working Papers 40, CEPREMAP.
  8. Henzel, Steffen R. & Mayr, Johannes, 2013. "The mechanics of VAR forecast pooling—A DSGE model based Monte Carlo study," The North American Journal of Economics and Finance, Elsevier, Elsevier, vol. 24(C), pages 1-24.
  9. Polito, Vito & Wickens, Mike, 2012. "A model-based indicator of the fiscal stance," European Economic Review, Elsevier, Elsevier, vol. 56(3), pages 526-551.

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