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Professional Forecasters and January

Author

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  • Franses, Ph.H.B.F.

Abstract

Each month various professional forecasters give forecasts for next year's real GDP growth and many other variables. In terms of forecast updates, January is a special month, as then the forecast horizon moves to the following calendar year, and as such the observation is not a revision. Instead of deleting the January data when analyzing forecast updates, this paper proposes a periodic version of an often considered test regression, to explicitly include and model the January data. An application of this periodic model for many forecasts across a range of countries learns that apparently there is a January optimism effect. In fact, in January, GDP forecast updates are suddenly positive, and at the same time the forecast updates for unemployment are likewise negative. This optimism about the new year of the professional forecasters is however found to be detrimental to forecast accuracy. The main conclusion is that forecasts created in January for the next year need to be treated with care.

Suggested Citation

  • Franses, Ph.H.B.F., 2019. "Professional Forecasters and January," Econometric Institute Research Papers EI2019-25, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  • Handle: RePEc:ems:eureir:118666
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    File URL: https://repub.eur.nl/pub/118666/EI2019-25-Report.pdf
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    References listed on IDEAS

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    1. repec:eee:finana:v:59:y:2018:i:c:p:94-104 is not listed on IDEAS
    2. 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.
    3. 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.
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    5. Kajal Lahiri & Gultekin Isiklar & 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.
    6. Ager, P. & Kappler, M. & Osterloh, S., 2009. "The accuracy and efficiency of the Consensus Forecasts: A further application and extension of the pooled approach," International Journal of Forecasting, Elsevier, vol. 25(1), pages 167-181.
    7. Cho, Dong W., 2002. "Do revisions improve forecasts?," International Journal of Forecasting, Elsevier, vol. 18(1), pages 107-115.
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    9. Nordhaus, William D, 1987. "Forecasting Efficiency: Concepts and Applications," The Review of Economics and Statistics, MIT Press, vol. 69(4), pages 667-674, November.
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    Blog mentions

    As found by EconAcademics.org, the blog aggregator for Economics research:
    1. Back to School Reading
      by Dave Giles in Econometrics Beat: Dave Giles' Blog on 2019-09-01 13:40:00

    More about this item

    Keywords

    Professional forecasters; macroeconomic forecasting; weak-form efficiency; periodic; regression model; forecast updates; January effect;

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • 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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