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When are GDP forecasts updated? Evidence from a large international panel

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  • Dovern, Jonas

Abstract

Based on a large international panel of surveyed GDP forecasts I analyze the frequency of forecast revisions and the factors that influence the likelihood of forecast revisions. I find that each month on average 40%–50% of forecasters revise their forecasts. In addition, I find that the likelihood of forecast revisions significantly depends on a number of factors such as the forecast horizon, the business-cycle, or strategic interactions between forecasters. My results suggest that a realistic modeling of expectations/forecasts of agents has to take into account cross-sectional heterogeneity, strategic interaction between agents, and effects of the economic environment—features that existing models such as the sticky information framework are missing.

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  • Dovern, Jonas, 2013. "When are GDP forecasts updated? Evidence from a large international panel," Economics Letters, Elsevier, vol. 120(3), pages 521-524.
    • Handle: RePEc:eee:ecolet:v:120:y:2013:i:3:p:521-524
    DOI: 10.1016/j.econlet.2013.06.007
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    Cited by:

    1. Lahiri, Kajal & Zhao, Yongchen, 2020. "The Nordhaus test with many zeros," Economics Letters, Elsevier, vol. 193(C).
    2. Frédérique Bec & Raouf Boucekkine & Caroline Jardet, 2023. "Why Are Inflation Forecasts Sticky? Theory and Application to France and Germany," International Journal of Central Banking, International Journal of Central Banking, vol. 19(4), pages 215-249, October.
    3. Glas, Alexander & Heinisch, Katja, 2021. "Conditional macroeconomic forecasts: Disagreement, revisions and forecast errors," IWH Discussion Papers 7/2021, Halle Institute for Economic Research (IWH).
    4. Glas, Alexander & Heinisch, Katja, 2023. "Conditional macroeconomic survey forecasts: Revisions and errors," Journal of International Money and Finance, Elsevier, vol. 138(C).
    5. Frédérique BEC, 2017. "Why are inflation forecasts sticky?," THEMA Working Papers 2017-23, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise.
    6. Dovern, Jonas, 2014. "A Multivariate Analysis of Forecast Disagreement: Confronting Models of Disagreement with SPF Data," Working Papers 0571, University of Heidelberg, Department of Economics.
    7. Dovern, Jonas & Fritsche, Ulrich & Loungani, Prakash & Tamirisa, Natalia, 2015. "Information rigidities: Comparing average and individual forecasts for a large international panel," International Journal of Forecasting, Elsevier, vol. 31(1), pages 144-154.
    8. Raffaella Giacomini & Vasiliki Skreta & Javier Turen, 2015. "Models, Inattention and Expectation Updates," Discussion Papers 1602, Centre for Macroeconomics (CFM).
    9. Paolo Bianchi & Bruno Deschamps & Khurshid M. Kiani, 2015. "Fiscal Balance and Current Account in Professional Forecasts," Review of International Economics, Wiley Blackwell, vol. 23(2), pages 361-378, May.
    10. de Mendonça, Helder Ferreira & Vereda, Luciano & Araujo, Mateus de Azevedo, 2022. "What type of information calls the attention of forecasters? Evidence from survey data in an emerging market," Journal of International Money and Finance, Elsevier, vol. 129(C).
    11. Meade, Nigel & Driver, Ciaran, 2023. "Differing behaviours of forecasters of UK GDP growth," International Journal of Forecasting, Elsevier, vol. 39(2), pages 772-790.
    12. Karlyn Mitchell & Douglas K. Pearce, 2017. "Direct Evidence on Sticky Information from the Revision Behavior of Professional Forecasters," Southern Economic Journal, John Wiley & Sons, vol. 84(2), pages 637-653, October.
    13. Dovern, Jonas, 2015. "A multivariate analysis of forecast disagreement: Confronting models of disagreement with survey data," European Economic Review, Elsevier, vol. 80(C), pages 16-35.
    14. Zidong An & João Tovar Jalles & Prakash Loungani, 2018. "How well do economists forecast recessions?," International Finance, Wiley Blackwell, vol. 21(2), pages 100-121, June.
    15. Jonas Dovern & Matthias Hartmann, 2017. "Forecast performance, disagreement, and heterogeneous signal-to-noise ratios," Empirical Economics, Springer, vol. 53(1), pages 63-77, August.
    16. Yingying Xu & Zhixin Liu & Zichao Jia & Chi-Wei Su, 2017. "Is time-variant information stickiness state-dependent?," Portuguese Economic Journal, Springer;Instituto Superior de Economia e Gestao, vol. 16(3), pages 169-187, December.
    17. Ulrich Heilemann & Susanne Schnorr-Bäcker, 2016. "Could The Start Of The German Recession 2008-2009 Have Been Foreseen? Evidence From Real-Time Data," Working Papers 2016-003, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    18. Giulia Piccillo & Poramapa Poonpakdee, 2021. "Effects of Macro Uncertainty on Mean Expectation and Subjective Uncertainty: Evidence from Households and Professional Forecasters," CESifo Working Paper Series 9486, CESifo.
    19. Rülke, Jan-Christoph & Silgoner, Maria & Wörz, Julia, 2016. "Herding behavior of business cycle forecasters," International Journal of Forecasting, Elsevier, vol. 32(1), pages 23-33.

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    More about this item

    Keywords

    Forecast revision; GDP forecast; Expectation; Sticky information; Panel data;
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • 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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