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Consensus forecasters: How good are they individually and why?

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  • Franses, Ph.H.B.F.
  • Maassen, N.R.

Abstract

We analyze the monthly forecasts for annual US GDP growth, CPI inflation rate and the unemployment rate delivered by forty professional forecasters collected in the Consensus database for 2000M01-2014M12. To understand why some forecasters are better than others, we create simple benchmark model-based forecasts. Evaluating the individual forecasts against the model forecasts is informative for how the professional forecasters behave. Next, we link this behavior to forecast performance. We find that forecasters who impose proper judgment to model-based forecasts also have highest forecast accuracy, and hence, they do not perform best just by luck.

Suggested Citation

  • Franses, Ph.H.B.F. & Maassen, N.R., 2015. "Consensus forecasters: How good are they individually and why?," Econometric Institute Research Papers EI2015-21, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    • Handle: RePEc:ems:eureir:78774
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    References listed on IDEAS

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    1. Dovern, Jonas & Weisser, Johannes, 2011. "Accuracy, unbiasedness and efficiency of professional macroeconomic forecasts: An empirical comparison for the G7," International Journal of Forecasting, Elsevier, vol. 27(2), pages 452-465.
    2. Rianne Legerstee & Philip Hans Franses, 2015. "Does Disagreement Amongst Forecasters Have Predictive Value?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(4), pages 290-302, July.
    3. Roy Batchelor, 2001. "How useful are the forecasts of intergovernmental agencies? The IMF and OECD versus the consensus," Applied Economics, Taylor & Francis Journals, vol. 33(2), pages 225-235.
    4. Batchelor, Roy, 2007. "Bias in macroeconomic forecasts," International Journal of Forecasting, Elsevier, vol. 23(2), pages 189-203.
    5. Gultekin Isiklar & Kajal Lahiri & Prakash Loungani, 2006. "How quickly do forecasters incorporate news? Evidence from cross‐country surveys," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(6), pages 703-725, September.
    6. Loungani, Prakash, 2001. "How accurate are private sector forecasts? Cross-country evidence from consensus forecasts of output growth," International Journal of Forecasting, Elsevier, vol. 17(3), pages 419-432.
    7. Isiklar, Gultekin & Lahiri, Kajal, 2007. "How far ahead can we forecast? Evidence from cross-country surveys," International Journal of Forecasting, Elsevier, vol. 23(2), pages 167-187.
    8. Franses, Philip Hans, 2016. "A note on the Mean Absolute Scaled Error," International Journal of Forecasting, Elsevier, vol. 32(1), pages 20-22.
    9. Franses,Philip Hans, 2014. "Expert Adjustments of Model Forecasts," Cambridge Books, Cambridge University Press, number 9781107081598, September.
    10. Kajal Lahiri & Xuguang Sheng, 2010. "Measuring forecast uncertainty by disagreement: The missing link," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(4), pages 514-538.
    11. Frenkel, Michael & Rülke, Jan-Christoph & Zimmermann, Lilli, 2013. "Do private sector forecasters chase after IMF or OECD forecasts?," Journal of Macroeconomics, Elsevier, vol. 37(C), pages 217-229.
    12. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    13. Roy Batchelor, 2007. "Forecaster Behaviour and Bias in Macroeconomic Forecasts," ifo Working Paper Series 39, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    14. Zarnowitz, Victor & Lambros, Louis A, 1987. "Consensus and Uncertainty in Economic Prediction," Journal of Political Economy, University of Chicago Press, vol. 95(3), pages 591-621, June.
    15. Fildes, Robert & Goodwin, Paul & Lawrence, Michael & Nikolopoulos, Konstantinos, 2009. "Effective forecasting and judgmental adjustments: an empirical evaluation and strategies for improvement in supply-chain planning," International Journal of Forecasting, Elsevier, vol. 25(1), pages 3-23.
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    Cited by:

    1. Philip Hans Franses & Bert Bruijn, 2017. "Benchmarking Judgmentally Adjusted Forecasts," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 22(1), pages 3-11, January.
    2. Meyler, Aidan, 2020. "Forecast performance in the ECB SPF: ability or chance?," Working Paper Series 2371, European Central Bank.

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    More about this item

    Keywords

    macroeconomic forecasts; expert adjustment;

    JEL classification:

    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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