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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.

Suggested Citation

  • 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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    References listed on IDEAS

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    1. Giampiero M. Gallo & Clive W.J. Granger & Yongil Jeon, 2002. "Copycats and Common Swings: The Impact of the Use of Forecasts in Information Sets," IMF Staff Papers, Palgrave Macmillan, vol. 49(1), pages 1-2.
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    16. Jonas Dovern & Ulrich Fritsche & Prakash Loungani & Natalia T. Tamirisa, 2013. "Information Rigidities in Economic Growth Forecasts; Evidence from a Large International Panel," IMF Working Papers 13/56, International Monetary Fund.
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    Citations

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    Cited by:

    1. Frédérique Bec & Raouf Boucekkine & Caroline Jardet, 2017. "Why are inflation forecasts sticky?," Working Papers 2017-17, Center for Research in Economics and Statistics.
    2. 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.
    3. 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.
    4. 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.
    5. repec:spr:empeco:v:53:y:2017:i:1:d:10.1007_s00181-016-1137-x is not listed on IDEAS
    6. Jonas Dovern & Matthias Hartmann, 2017. "Forecast performance, disagreement, and heterogeneous signal-to-noise ratios," Empirical Economics, Springer, vol. 53(1), pages 63-77, August.
    7. F. Bec & R. Boucekkine & C. Jardet, 2017. "Why are inflation forecasts sticky? Theory and application to France and Germany," Working papers 650, Banque de France.
    8. repec:spr:portec:v:16:y:2017:i:3:d:10.1007_s10258-017-0129-x is not listed on IDEAS
    9. Frédérique BEC, 2017. "Why are inflation forecasts sticky?," THEMA Working Papers 2017-23, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise.
    10. 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.

    More about this item

    Keywords

    Forecast revision; GDP forecast; Expectation; Sticky information; Panel data;

    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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