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Reporting biases and survey results: evidence from European professional forecasters

Author

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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

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    1. Ottaviani, Marco & Sorensen, Peter Norman, 2006. "The strategy of professional forecasting," Journal of Financial Economics, Elsevier, vol. 81(2), pages 441-466, August.
    2. Lahiri, Kajal & Teigland, Christie & Zaporowski, Mark, 1988. "Interest Rates and the Subjective Probability Distribution of Inflation Forecasts," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 20(2), pages 233-248, May.
    3. Ang, Andrew & Bekaert, Geert & Wei, Min, 2007. "Do macro variables, asset markets, or surveys forecast inflation better?," Journal of Monetary Economics, Elsevier, vol. 54(4), pages 1163-1212, May.
    4. 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.
    5. Gianna Boero & Jeremy Smith & KennethF. 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.
    6. 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.
    7. Robert W. Rich & Joseph Tracy, 2006. "The relationship between expected inflation, disagreement, and uncertainty: evidence from matched point and density forecasts," Staff Reports 253, Federal Reserve Bank of New York.
    8. Tilman Ehrbeck & Robert Waldmann, 1996. "Why Are Professional Forecasters Biased? Agency versus Behavioral Explanations," The Quarterly Journal of Economics, Oxford University Press, vol. 111(1), pages 21-40.
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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," 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," 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. 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.
    6. Balazs VARGA & Zsolt DARVAS, "undated". "Time-Varying Coefficient Methods to Measure Inflation Persistence," EcoMod2010 259600167, EcoMod.
    7. 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.
    8. Ambrocio, Gene, 2017. "The real effects of overconfidence and fundamental uncertainty shocks," Research Discussion Papers 37/2017, Bank of Finland.
    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. 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.
    12. Maxime Phillot & Rina Rosenblatt-Wisch, 2018. "Inflation Expectations: The Effect of Question Ordering on Forecast Inconsistencies," Working Papers 2018-11, Swiss National Bank.

    More about this item

    Keywords

    point estimates; subjective probability distributions; survey methods; Survey of Professional Forecasters (SPF);

    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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