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Evaluating DSGE model forecasts of comovements

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  • Edward Herbst
  • Frank Schorfheide

Abstract

This paper develops and applies tools to assess multivariate aspects of Bayesian Dynamic Stochastic General Equilibrium (DSGE) model forecasts and their ability to predict comovements among key macroeconomic variables. The authors construct posterior predictive checks to evaluate the calibration of conditional and unconditional density forecasts, in addition to checks for root-mean-squared errors and event probabilities associated with these forecasts. The checks are implemented on a three-equation DSGE model as well as the Smets and Wouters (2007) model using real-time data. They find that the additional features incorporated into the Smets-Wouters model do not lead to a uniform improvement in the quality of density forecasts and prediction of comovements of output, inflation, and interest rates.

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Paper provided by Federal Reserve Bank of Philadelphia in its series Working Papers with number 11-5.

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Date of creation: 2011
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Handle: RePEc:fip:fedpwp:11-5

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Keywords: Econometric models ; Forecasting;

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  1. Rochelle M. Edge & Michael T. Kiley & Jean-Philippe Laforte, 2010. "A comparison of forecast performance between Federal Reserve staff forecasts, simple reduced-form models, and a DSGE model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., John Wiley & Sons, Ltd., vol. 25(4), pages 720-754.
  2. Wolters, Maik H., 2013. "Evaluating point and density forecasts of DSGE models," Economics Working Papers, Christian-Albrechts-University of Kiel, Department of Economics 2013-03, Christian-Albrechts-University of Kiel, Department of Economics.
  3. Del Negro, Marco & Schorfheide, Frank & Smets, Frank & Wouters, Rafael, 2007. "On the Fit of New Keynesian Models," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 25, pages 123-143, April.
  4. Frank Schorfheide & Keith Sill & Maxym Kryshko, 2008. "DSGE model-based forecasting of non-modelled variables," Working Papers, Federal Reserve Bank of Philadelphia 08-17, Federal Reserve Bank of Philadelphia.
  5. Frank Schorfheide, 2008. "DSGE model-based estimation of the New Keynesian Phillips curve," Economic Quarterly, Federal Reserve Bank of Richmond, Federal Reserve Bank of Richmond, issue Fall, pages 397-433.
  6. Daniel F. Waggoner & Tao Zha, 1998. "Conditional forecasts in dynamic multivariate models," Working Paper, Federal Reserve Bank of Atlanta 98-22, Federal Reserve Bank of Atlanta.
  7. Lawrence J. Christiano & Martin Eichenbaum & Charles Evans, 2001. "Nominal rigidities and the dynamic effects of a shock to monetary policy," Working Paper, Federal Reserve Bank of Cleveland 0107, Federal Reserve Bank of Cleveland.
  8. Sungbae An & Frank Schorfheide, 2006. "Bayesian analysis of DSGE models," Working Papers, Federal Reserve Bank of Philadelphia 06-5, Federal Reserve Bank of Philadelphia.
  9. Tilmann Gneiting & Fadoua Balabdaoui & Adrian E. Raftery, 2007. "Probabilistic forecasts, calibration and sharpness," Journal of the Royal Statistical Society Series B, Royal Statistical Society, Royal Statistical Society, vol. 69(2), pages 243-268.
  10. Christoffel, Kai & Warne, Anders & Coenen, Günter, 2010. "Forecasting with DSGE models," Working Paper Series, European Central Bank 1185, European Central Bank.
  11. 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.
  12. Smets, Frank & Wouters, Raf, 2007. "Shocks and frictions in US business cycles: a Bayesian DSGE approach," Working Paper Series, European Central Bank 0722, European Central Bank.
  13. Kling, John L & Bessler, David A, 1989. "Calibration-Based Predictive Distributions: An Application of Prequential Analysis to Interest Rates, Money, Prices, and Output," The Journal of Business, University of Chicago Press, University of Chicago Press, vol. 62(4), pages 477-99, October.
  14. Frank Smets & Raf Wouters, 2003. "An Estimated Dynamic Stochastic General Equilibrium Model of the Euro Area," Journal of the European Economic Association, MIT Press, MIT Press, vol. 1(5), pages 1123-1175, 09.
  15. Francis X. Diebold & Jinyong Hahn & Anthony S. Tay, 1999. "Multivariate Density Forecast Evaluation And Calibration In Financial Risk Management: High-Frequency Returns On Foreign Exchange," The Review of Economics and Statistics, MIT Press, MIT Press, vol. 81(4), pages 661-673, November.
  16. Faust, Jon & Wright, Jonathan H., 2009. "Comparing Greenbook and Reduced Form Forecasts Using a Large Realtime Dataset," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 27(4), pages 468-479.
  17. John Geweke, 2007. "Bayesian Model Comparison and Validation," American Economic Review, American Economic Association, American Economic Association, vol. 97(2), pages 60-64, May.
  18. Diebold, Francis X & Gunther, Todd A & Tay, Anthony S, 1998. "Evaluating Density Forecasts with Applications to Financial Risk Management," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 863-83, November.
  19. Kydland, Finn E & Prescott, Edward C, 1982. "Time to Build and Aggregate Fluctuations," Econometrica, Econometric Society, Econometric Society, vol. 50(6), pages 1345-70, November.
  20. Rochelle M. Edge & Refet S. Gurkaynak, 2010. "How Useful Are Estimated DSGE Model Forecasts for Central Bankers?," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, Economic Studies Program, The Brookings Institution, vol. 41(2 (Fall)), pages 209-259.
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Citations

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Cited by:
  1. Mehmet Balcilar & Rangan Gupta & Anandamayee Majumdar & Stephen M. Miller, 2012. "Was the Recent Downturn in US GDP Predictable?," Working Papers, University of Pretoria, Department of Economics 201230, University of Pretoria, Department of Economics.
  2. Kolasa, Marcin & Rubaszek, Michał, 2014. "Forecasting with DSGE models with financial frictions," Dynare Working Papers, CEPREMAP 40, CEPREMAP.
  3. Frank Schorfheide & Dongho Song, 2013. "Real-Time Forecasting with a Mixed-Frequency VAR," NBER Working Papers 19712, National Bureau of Economic Research, Inc.
  4. Wolters, Maik H., 2011. "Forecasting under Model Uncertainty," Annual Conference 2011 (Frankfurt, Main): The Order of the World Economy - Lessons from the Crisis, Verein für Socialpolitik / German Economic Association 48723, Verein für Socialpolitik / German Economic Association.
  5. Casares, Miguel & Vázquez Pérez, Jesús, 2012. "Data Revisions in the Estimation of DSGE Models," DFAEII Working Papers 2012-06, University of the Basque Country - Department of Foundations of Economic Analysis II.
  6. 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.
  7. Wolters, Maik Hendrik, 2012. "Evaluating point and density forecasts of DSGE models," IMFS Working Paper Series, Institute for Monetary and Financial Stability (IMFS), Goethe University Frankfurt 59, Institute for Monetary and Financial Stability (IMFS), Goethe University Frankfurt.
  8. Stelios Bekiros & Alessia Paccagnini, 2013. "On the predictability of time-varying VAR and DSGE models," Empirical Economics, Springer, Springer, vol. 45(1), pages 635-664, August.
  9. Bekiros, Stelios D. & Paccagnini, Alessia, 2014. "Bayesian forecasting with small and medium scale factor-augmented vector autoregressive DSGE models," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 71(C), pages 298-323.

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