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Reporting biases and survey results: evidence from European professional forecasters

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  • Manzanares, Andrés
  • Garcí­a, Juan Angel

Abstract

Using data from the ECB's Survey of Professional Forecasters, we investigate the reporting practices of survey participants by comparing their point predictions and the mean/median/mode of their probability forecasts. We find that the individual point predictions, on average, tend to be biased towards favourable outcomes: they suggest too high growth and too low inflation rates. Most importantly, for each survey round, the aggregate survey results based on the average of the individual point predictions are also biased. These findings cast doubt on combined survey measures that average individual point predictions. Survey results based on probability forecasts are more reliable. JEL Classification: C42, E27, E47

Suggested Citation

  • Manzanares, Andrés & Garcí­a, Juan Angel, 2007. "Reporting biases and survey results: evidence from European professional forecasters," Working Paper Series 836, European Central Bank.
    • Handle: RePEc:ecb:ecbwps:2007836
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    References listed on IDEAS

    as
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    6. Gianna Boero & Jeremy Smith & Kenneth F. Wallis, 2008. "Uncertainty and Disagreement in Economic Prediction: The Bank of England Survey of External Forecasters," Economic Journal, Royal Economic Society, vol. 118(530), pages 1107-1127, July.
    7. Patton, Andrew J. & Timmermann, Allan, 2007. "Testing Forecast Optimality Under Unknown Loss," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 1172-1184, December.
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    Citations

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    Cited by:

    1. Clements, Michael P., 2010. "Explanations of the inconsistencies in survey respondents' forecasts," European Economic Review, Elsevier, vol. 54(4), pages 536-549, May.
    2. Reitz, Stefan & Rülke, Jan & Stadtmann, Georg, 2012. "Nonlinear Expectations in Speculative Markets," VfS Annual Conference 2012 (Goettingen): New Approaches and Challenges for the Labor Market of the 21st Century 62045, Verein für Socialpolitik / German Economic Association.
    3. Riccardo Colacito & Eric Ghysels & Jinghan Meng & Wasin Siwasarit, 2016. "Skewness in Expected Macro Fundamentals and the Predictability of Equity Returns: Evidence and Theory," The Review of Financial Studies, Society for Financial Studies, vol. 29(8), pages 2069-2109.
    4. Reitz, Stefan & Rülke, Jan-Christoph & Stadtmann, Georg, 2012. "Nonlinear expectations in speculative markets – Evidence from the ECB survey of professional forecasters," Journal of Economic Dynamics and Control, Elsevier, vol. 36(9), pages 1349-1363.
    5. Clements, Michael P., 2014. "Probability distributions or point predictions? Survey forecasts of US output growth and inflation," International Journal of Forecasting, Elsevier, vol. 30(1), pages 99-117.
    6. Christian Pierdzioch & Jan-Christoph Rülke & Georg Stadtmann, 2013. "Oil price forecasting under asymmetric loss," Applied Economics, Taylor & Francis Journals, vol. 45(17), pages 2371-2379, June.
    7. Ambrocio, Gene, 2017. "The real effects of overconfidence and fundamental uncertainty shocks," Research Discussion Papers 37/2017, Bank of Finland.
    8. repec:zbw:bofrdp:2017_037 is not listed on IDEAS
    9. Pierdzioch, Christian & Rülke, Jan Christoph & Stadtmann, Georg, 2010. "New evidence of anti-herding of oil-price forecasters," Energy Economics, Elsevier, vol. 32(6), pages 1456-1459, November.
    10. Frenkel, Michael & Lis, Eliza M. & Rülke, Jan-Christoph, 2011. "Has the economic crisis of 2007-2009 changed the expectation formation process in the Euro area?," Economic Modelling, Elsevier, vol. 28(4), pages 1808-1814, July.
    11. Maxime Phillot & Rina Rosenblatt-Wisch, 2018. "Inflation Expectations: The Effect of Question Ordering on Forecast Inconsistencies," Working Papers 2018-11, Swiss National Bank.
    12. Maxime Phillot & Rina Rosenblatt-Wisch, 2024. "Order Matters: An Experimental Study on How Question Ordering Affects Survey-Based Inflation Forecasts," International Journal of Central Banking, International Journal of Central Banking, vol. 20(3), pages 63-114, July.
    13. Balazs VARGA & Zsolt DARVAS, 2010. "Time-Varying Coefficient Methods to Measure Inflation Persistence," EcoMod2010 259600167, EcoMod.
    14. Jonas Dovern & Geoff Kenny, 2020. "Anchoring Inflation Expectations in Unconventional Times: Micro Evidence for the Euro Area," International Journal of Central Banking, International Journal of Central Banking, vol. 16(5), pages 309-347, October.
    15. Pedro Pires Ribeiro & José Dias Curto, 2018. "How do zero-coupon inflation swaps predict inflation rates in the euro area? Evidence of efficiency and accuracy on 1-year contracts," Empirical Economics, Springer, vol. 54(4), pages 1451-1475, June.
    16. Herman O. Stekler, 2008. "What Do We Know About G-7 Macro Forecasts?," Working Papers 2008-009, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    17. Ralf Fendel & Eliza M. Lis & Jan-Christoph Rülke, 2009. "Do Euro Area Forecasters (Still) Have Faith in Macroeconomic Building Blocks? – Expectation Formation when Economics is in Crisis," WHU Working Paper Series - Economics Group 09-03, WHU - Otto Beisheim School of Management.
    18. Víctor López-Pérez, 2017. "Do professional forecasters behave as if they believed in the New Keynesian Phillips Curve for the euro area?," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 44(1), pages 147-174, February.
    19. repec:zbw:bofrdp:037 is not listed on IDEAS

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    More about this item

    Keywords

    point estimates; subjective probability distributions; survey methods; Survey of Professional Forecasters (SPF);
    All these keywords.

    JEL classification:

    • C42 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Survey Methods
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
    • E47 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Forecasting and Simulation: Models and Applications

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