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Can Macroeconomists Forecast Risk? Event-Based Evidence from the Euro-Area SPF

Author

Listed:
  • Geoff Kenny

    (European Central Bank)

  • Thomas Kostka

    (European Central Bank)

  • Federico Masera

    (Universidad Carlos III de Madrid)

Abstract

We apply methods to evaluate the risk assessments collected as part of the ECB Survey of Professional Forecasters (SPF). Our approach focuses on direction-of-change predictions as well as the prediction of more specific high and low macroeconomic outcomes located in the upper and lower regions of the predictive densities. For inflation and GDP growth, we find such surveyed densities are informative about future direction of change. Regarding high and low outcome events, the surveys are most informative about GDP growth outcomes and at short horizons. The upper and lower regions of the predictive densities for inflation appear less informative.

Suggested Citation

  • Geoff Kenny & Thomas Kostka & Federico Masera, 2015. "Can Macroeconomists Forecast Risk? Event-Based Evidence from the Euro-Area SPF," International Journal of Central Banking, International Journal of Central Banking, vol. 11(4), pages 1-46, December.
  • Handle: RePEc:ijc:ijcjou:y:2015:q:5:a:1
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    References listed on IDEAS

    as
    1. Geoff Kenny & Thomas Kostka & Federico Masera, 2014. "How Informative are the Subjective Density Forecasts of Macroeconomists?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(3), pages 163-185, April.
    2. Anne Sofie Jore & James Mitchell & Shaun P. Vahey, 2010. "Combining forecast densities from VARs with uncertain instabilities," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(4), pages 621-634.
    3. Murphy, Allan H. & Winkler, Robert L., 1992. "Diagnostic verification of probability forecasts," International Journal of Forecasting, Elsevier, vol. 7(4), pages 435-455, March.
    4. Davies, Anthony & Lahiri, Kajal, 1995. "A new framework for analyzing survey forecasts using three-dimensional panel data," Journal of Econometrics, Elsevier, vol. 68(1), pages 205-227, July.
    5. Geoff Kenny & Thomas Kostka & Federico Masera, 2015. "Density characteristics and density forecast performance: a panel analysis," Empirical Economics, Springer, vol. 48(3), pages 1203-1231, May.
    6. Engelberg, Joseph & Manski, Charles F. & Williams, Jared, 2009. "Comparing the Point Predictions and Subjective Probability Distributions of Professional Forecasters," Journal of Business & Economic Statistics, American Statistical Association, vol. 27, pages 30-41.
    7. Antonello D’Agostino & Kieran Mcquinn & Karl Whelan, 2012. "Are Some Forecasters Really Better Than Others?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 44(4), pages 715-732, June.
    8. Keane, Michael P & Runkle, David E, 1990. "Testing the Rationality of Price Forecasts: New Evidence from Panel Data," American Economic Review, American Economic Association, vol. 80(4), pages 714-735, September.
    9. Carlos Bowles & Roberta Friz & Veronique Genre & Geoff Kenny & Aidan Meyler & Tuomas Rautanen, 2010. "An Evaluation of the Growth and Unemployment Forecasts in the ECB Survey of Professional Forecasters," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2010(2), pages 1-28.
    10. James Mitchell & Kenneth F. Wallis, 2011. "Evaluating density forecasts: forecast combinations, model mixtures, calibration and sharpness," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(6), pages 1023-1040, September.
    11. Genre, Véronique & Kenny, Geoff & Meyler, Aidan & Timmermann, Allan, 2013. "Combining expert forecasts: Can anything beat the simple average?," International Journal of Forecasting, Elsevier, vol. 29(1), pages 108-121.
    12. Juan Angel Garcia, 2003. "An introduction to the ECB’s survey of professional forecasters," Occasional Paper Series 08, European Central Bank.
    13. Michael Clements, 2006. "Evaluating the survey of professional forecasters probability distributions of expected inflation based on derived event probability forecasts," Empirical Economics, Springer, vol. 31(1), pages 49-64, March.
    14. Andrade, P. & Ghysels, E. & Idier, J., 2012. "Tails of Inflation Forecasts and Tales of Monetary Policy," Working papers 407, Banque de France.
    15. Fred Joutz & Michael P. Clements & Herman O. Stekler, 2007. "An evaluation of the forecasts of the federal reserve: a pooled approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(1), pages 121-136.
    16. Don Harding & Adrian Pagan, 2010. "Can We Predict Recessions?," NCER Working Paper Series 69, National Centre for Econometric Research.
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    Cited by:

    1. Geoff Kenny & Thomas Kostka & Federico Masera, 2015. "Density characteristics and density forecast performance: a panel analysis," Empirical Economics, Springer, vol. 48(3), pages 1203-1231, May.
    2. repec:pal:jorsoc:v:68:y:2017:i:12:d:10.1057_s41274-017-0189-4 is not listed on IDEAS
    3. Paola Cerchiello & Paolo Giudici, 2014. "Conditional graphical models for systemic risk measurement," DEM Working Papers Series 087, University of Pavia, Department of Economics and Management.
    4. Raffaella Calabrese & Johan A. Elkink & Paolo S. Giudici, 2017. "Measuring bank contagion in Europe using binary spatial regression models," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(12), pages 1503-1511, December.

    More about this item

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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