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

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  • 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

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    1. 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.
    2. Berkowitz, Jeremy, 2001. "Testing Density Forecasts, with Applications to Risk Management," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(4), pages 465-474, October.
    3. 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.
    4. 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.
    5. Andrade, P. & Ghysels, E. & Idier, J., 2012. "Tails of Inflation Forecasts and Tales of Monetary Policy," Working papers 407, Banque de France.
    6. 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.
    7. Kenny, Geoff & Genre, Véronique & Meyler, Aidan & Timmermann, Allan, 2010. "Combining the forecasts in the ECB survey of professional forecasters: can anything beat the simple average?," Working Paper Series 1277, European Central Bank.
    8. 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.
    9. 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.
    10. Diebold, Francis X & Rudebusch, Glenn D, 1989. "Scoring the Leading Indicators," The Journal of Business, University of Chicago Press, vol. 62(3), pages 369-391, July.
    11. 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.
    12. 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.
    13. Geweke, John & Amisano, Gianni, 2011. "Optimal prediction pools," Journal of Econometrics, Elsevier, vol. 164(1), pages 130-141, September.
    14. Croushore Dean, 2010. "An Evaluation of Inflation Forecasts from Surveys Using Real-Time Data," The B.E. Journal of Macroeconomics, De Gruyter, vol. 10(1), pages 1-32, May.
    15. 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.
    16. Pesaran, M. Hashem & Timmermann, Allan, 2009. "Testing Dependence Among Serially Correlated Multicategory Variables," Journal of the American Statistical Association, American Statistical Association, vol. 104(485), pages 325-337.
    17. Anthony Tay & Kenneth F. Wallis, 2000. "Density Forecasting: A Survey," Econometric Society World Congress 2000 Contributed Papers 0370, Econometric Society.
    18. Juan Angel Garcia, 2003. "An introduction to the ECB’s survey of professional forecasters," Occasional Paper Series 08, European Central Bank.
    19. Engle, Robert F. & White (the late), Halbert (ed.), 1999. "Cointegration, Causality, and Forecasting: Festschrift in Honour of Clive W. J. Granger," OUP Catalogue, Oxford University Press, number 9780198296836, Decembrie.
    20. 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.
    21. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 33(1), pages 125-132.
    22. Don Harding & Adrian Pagan, 2010. "Can We Predict Recessions?," NCER Working Paper Series 69, National Centre for Econometric Research.
    23. Conflitti, Cristina & De Mol, Christine & Giannone, Domenico, 2015. "Optimal combination of survey forecasts," International Journal of Forecasting, Elsevier, vol. 31(4), pages 1096-1103.
    24. Giordani, Paolo & Soderlind, Paul, 2006. "Is there evidence of pessimism and doubt in subjective distributions? Implications for the equity premium puzzle," Journal of Economic Dynamics and Control, Elsevier, vol. 30(6), pages 1027-1043, June.
    25. Murphy, Allan H. & Winkler, Robert L., 1992. "Diagnostic verification of probability forecasts," International Journal of Forecasting, Elsevier, vol. 7(4), pages 435-455, March.
    26. Kajal Lahiri & J George Wang, 2006. "Subjective Probability Forecasts for Recessions," Business Economics, Palgrave Macmillan;National Association for Business Economics, vol. 41(2), pages 26-37, April.
    27. 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.
    28. 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.
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    Cited by:

    1. 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.
    2. Galvão, Ana Beatriz & Garratt, Anthony & Mitchell, James, 2021. "Does judgment improve macroeconomic density forecasts?," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1247-1260.
    3. Constantin Rudolf Salomo Bürgi, 2023. "How to deal with missing observations in surveys of professional forecasters," Journal of Applied Economics, Taylor & Francis Journals, vol. 26(1), pages 2185975-218, December.
    4. 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.
    5. Ganics, Gergely & Odendahl, Florens, 2021. "Bayesian VAR forecasts, survey information, and structural change in the euro area," International Journal of Forecasting, Elsevier, vol. 37(2), pages 971-999.
    6. 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.
    7. Bańbura, Marta & Brenna, Federica & Paredes, Joan & Ravazzolo, Francesco, 2021. "Combining Bayesian VARs with survey density forecasts: does it pay off?," Working Paper Series 2543, European Central Bank.

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    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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