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How frequently should we re-estimate DSGE models?

Author

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  • Marcin Kolasa
  • Michał Rubaszek

Abstract

A common practice in policy making institutions using DSGE models for forecasting is to re-estimate them only occasionally rather than every forecasting round. In this paper we ask how such a practice affects the accuracy of DSGE model-based forecasts. To this end we use a canonical medium-sized New Keynesian model and compare how its quarterly real-time forecasts for the US economy vary with the interval between consecutive re-estimations. We find that updating the model parameters only once a year usually does not lead to any significant deterioration in the accuracy of point forecasts. On the other hand, there are some gains from increasing the frequency of re-estimation if one is interested in the quality of density forecasts.

Suggested Citation

  • Marcin Kolasa & Michał Rubaszek, 2014. "How frequently should we re-estimate DSGE models?," NBP Working Papers 194, Narodowy Bank Polski, Economic Research Department.
  • Handle: RePEc:nbp:nbpmis:194
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    File URL: http://www.nbp.pl/publikacje/materialy_i_studia/194_en.pdf
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    References listed on IDEAS

    as
    1. Wieland, Volker & Wolters, Maik, 2013. "Forecasting and Policy Making," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.),Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 239-325, Elsevier.
    2. Marcin Kolasa & MichaŁ Rubaszek & PaweŁ SkrzypczyŃski, 2012. "Putting the New Keynesian DSGE Model to the Real-Time Forecasting Test," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 44(7), pages 1301-1324, October.
    3. Amisano, Gianni & Giacomini, Raffaella, 2007. "Comparing Density Forecasts via Weighted Likelihood Ratio Tests," Journal of Business & Economic Statistics, American Statistical Association, vol. 25, pages 177-190, April.
    4. 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., vol. 25(4), pages 720-754.
    5. Warne, Anders & Coenen, Günter & Christoffel, Kai, 2010. "Forecasting with DSGE models," Working Paper Series 1185, European Central Bank.
    6. Kolasa, Marcin & Rubaszek, Michał, 2015. "Forecasting using DSGE models with financial frictions," International Journal of Forecasting, Elsevier, vol. 31(1), pages 1-19.
    7. Rubaszek, Michal & Skrzypczynski, Pawel, 2008. "On the forecasting performance of a small-scale DSGE model," International Journal of Forecasting, Elsevier, vol. 24(3), pages 498-512.
    8. Frank Smets & Raf Wouters, 2003. "An Estimated Dynamic Stochastic General Equilibrium Model of the Euro Area," Journal of the European Economic Association, MIT Press, vol. 1(5), pages 1123-1175, September.
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    Citations

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    Cited by:

    1. Benchimol, Jonathan, 2016. "Money and monetary policy in Israel during the last decade," Journal of Policy Modeling, Elsevier, vol. 38(1), pages 103-124.
    2. Cardani, Roberta & Paccagnini, Alessia & Villa, Stefania, 2019. "Forecasting with instabilities: An application to DSGE models with financial frictions," Journal of Macroeconomics, Elsevier, vol. 61(C), pages 1-1.
    3. Paccagnini, Alessia, 2017. "Dealing with Misspecification in DSGE Models: A Survey," MPRA Paper 82914, University Library of Munich, Germany.
    4. McAdam, Peter & Warne, Anders, 2019. "Euro area real-time density forecasting with financial or labor market frictions," International Journal of Forecasting, Elsevier, vol. 35(2), pages 580-600.
    5. Jonathan Benchimol & André Fourçans, 2017. "Monetary Rule, Central Bank Loss and Household’s Welfare: an Empirical Investigation," Globalization Institute Working Papers 329, Federal Reserve Bank of Dallas, revised 01 Oct 2017.

    More about this item

    Keywords

    forecasting; DSGE models; parameter updating;

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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