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A new approach to multi-step forecasting using dynamic stochastic general equilibrium models

Author

Listed:
  • Kapetanious, George

    () (Bank of England)

  • Price, Simon

    () (Bank of England)

  • Theodoridis, Konstantinos

    () (Bank of England)

Abstract

DSGE models are of interest because they offer structural interpretations, but are also increasingly used for forecasting. Estimation often proceeds by methods which involve building the likelihood by one-step ahead (h=1) prediction errors. However in principle this can be done using different horizons where h>1. Using the well-known model of Smets and Wouters (2007), for h=1 classical ML parameter estimates are similar to those originally reported. As h extends some estimated parameters change, but not to an economically significant degree. Forecast performance is often improved, in several cases significantly.

Suggested Citation

  • Kapetanious, George & Price, Simon & Theodoridis, Konstantinos, 2015. "A new approach to multi-step forecasting using dynamic stochastic general equilibrium models," Bank of England working papers 567, Bank of England.
    • Handle: RePEc:boe:boeewp:0567
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    References listed on IDEAS

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    1. Lars E O Svensson, 2005. "Monetary Policy with Judgment: Forecast Targeting," International Journal of Central Banking, International Journal of Central Banking, vol. 1(1), May.
    2. Frank Smets & Rafael Wouters, 2007. "Shocks and Frictions in US Business Cycles: A Bayesian DSGE Approach," American Economic Review, American Economic Association, vol. 97(3), pages 586-606, June.
    3. Fawcett, Nicholas & Koerber, Lena & Masolo, Riccardo & Waldron, Matthew, 2015. "Evaluating UK point and density forecasts from an estimated DSGE model: the role of off-model information over the financial crisis," Bank of England working papers 538, Bank of England.
    4. Peter N. Ireland, 2013. "Stochastic Growth In The United States And Euro Area," Journal of the European Economic Association, European Economic Association, vol. 11(1), pages 1-24, February.
    5. Peter N. Ireland, 2004. "Technology Shocks in the New Keynesian Model," The Review of Economics and Statistics, MIT Press, vol. 86(4), pages 923-936, November.
    6. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    7. Rochelle M. Edge & Refet S. Gurkaynak, 2011. "How useful are estimated DSGE model forecasts?," Finance and Economics Discussion Series 2011-11, Board of Governors of the Federal Reserve System (U.S.).
    8. Jesús Fernández-Villaverde & Juan F. Rubio-Ramírez, 2008. "How Structural Are Structural Parameters?," NBER Chapters,in: NBER Macroeconomics Annual 2007, Volume 22, pages 83-137 National Bureau of Economic Research, Inc.
    9. Edward Herbst & Frank Schorfheide, 2014. "Sequential Monte Carlo Sampling For Dsge Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(7), pages 1073-1098, November.
    10. Schorfheide, Frank, 2005. "VAR forecasting under misspecification," Journal of Econometrics, Elsevier, vol. 128(1), pages 99-136, September.
    11. Svensson, Lars E. O., 2005. "Monetary policy with judgment: forecast targeting," Working Paper Series 476, European Central Bank.
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    Cited by:

    1. Michal Franta, 2016. "Iterated Multi-Step Forecasting with Model Coefficients Changing Across Iterations," Working Papers 2016/05, Czech National Bank, Research Department.

    More about this item

    Keywords

    DSGE models; multi-step prediction errors; forecasting.;

    JEL classification:

    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling

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