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Medium-term forecasting of euro-area macroeconomic variables with DSGE and BVARX models

Author

Listed:
  • Lorenzo Burlon

    (Bank of Italy)

  • Simone Emiliozzi

    (Bank of Italy)

  • Alessandro Notarpietro

    (Bank of Italy)

  • Massimiliano Pisani

    (Bank of Italy)

Abstract
The paper assesses the performance of medium-term forecasts of euro-area GDP and inflation obtained with a DSGE model and a BVARX model currently in use at the Bank of Italy. The performance is compared with that of simple univariate models and with the Eurosystem projections; the same real time assumptions underlying the latter are used to condition the DSGE and the BVARX forecasts. We find that the performance of both forecasts is similar to that of Eurosystem forecasts and overall more accurate than that of simple autoregressive models. The DSGE model shows a relatively better performance in forecasting inflation, while the BVARX model fares better in forecasting

Suggested Citation

  • Lorenzo Burlon & Simone Emiliozzi & Alessandro Notarpietro & Massimiliano Pisani, 2015. "Medium-term forecasting of euro-area macroeconomic variables with DSGE and BVARX models," Questioni di Economia e Finanza (Occasional Papers) 257, Bank of Italy, Economic Research and International Relations Area.
    • Handle: RePEc:bdi:opques:qef_257_15
    as

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    File URL: https://www.bancaditalia.it/pubblicazioni/qef/2015-0257/QEF_257.pdf
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    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. Malin Adolfson & Michael K. Andersson & Jesper Lindé & Mattias Villani & Anders Vredin, 2007. "Modern Forecasting Models in Action: Improving Macroeconomic Analyses at Central Banks," International Journal of Central Banking, International Journal of Central Banking, vol. 3(4), pages 111-144, December.
    4. Fabio Busetti, 2017. "Quantile Aggregation of Density Forecasts," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 79(4), pages 495-512, August.
    5. Lucia Alessi & Eric Ghysels & Luca Onorante & Richard Peach & Simon Potter, 2014. "Central Bank Macroeconomic Forecasting During the Global Financial Crisis: The European Central Bank and Federal Reserve Bank of New York Experiences," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 32(4), pages 483-500, October.
    6. Litterman, Robert B, 1986. "Forecasting with Bayesian Vector Autoregressions-Five Years of Experience," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(1), pages 25-38, January.
    7. Warne, Anders & Coenen, Günter & Christoffel, Kai, 2010. "Forecasting with DSGE models," Working Paper Series 1185, European Central Bank.
    8. 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.
    9. Lorenzo Forni & Andrea Gerali & Alessandro Notarpietro & Massimiliano Pisani, 2012. "Euro area and global oil shocks: an empirical model-based analysis," Temi di discussione (Economic working papers) 873, Bank of Italy, Economic Research and International Relations Area.
    10. Hall, Stephen G. & Mitchell, James, 2007. "Combining density forecasts," International Journal of Forecasting, Elsevier, vol. 23(1), pages 1-13.
    11. Francis X. Diebold, 2015. "Comparing Predictive Accuracy, Twenty Years Later: A Personal Perspective on the Use and Abuse of Diebold-Mariano Tests," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(1), pages 1-1, January.
    12. G. Elliott & C. Granger & A. Timmermann (ed.), 2013. "Handbook of Economic Forecasting," Handbook of Economic Forecasting, Elsevier, edition 1, volume 2, number 2.
    13. Litterman, Robert, 1986. "Forecasting with Bayesian vector autoregressions -- Five years of experience : Robert B. Litterman, Journal of Business and Economic Statistics 4 (1986) 25-38," International Journal of Forecasting, Elsevier, vol. 2(4), pages 497-498.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    forecasting; DSGE; BVARX; euro area;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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