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Information Rigidities: Comparing Average And Individual Forecasts For A Large International Panel

Author

Listed:
  • Jonas Dovern

    () (University of Heidelberg)

  • Ulrich Fritsche

    () (Hamburg University)

  • Prakash Loungani

    () (International Monetary Fund)

  • Natalia Tamirisa

    () (International Monetary Fund)

Abstract

We study forecasts for real GDP growth using a large panel of individual forecasts from 36 advanced and emerging economies during 1989–2010. We show that the degree of information rigidity in average forecasts is substantially higher than that in individual forecasts. Individual level forecasts are updated quite frequently, a behavior more in line “noisy” information models (Woodford, 2002; Sims, 2003) than with the assumptions of the sticky information model (Mankiw and Reis, 2002). While there are cross-country variations in information rigidity, there is no systematic difference between advanced and emerging economies.

Suggested Citation

  • Jonas Dovern & Ulrich Fritsche & Prakash Loungani & Natalia Tamirisa, 2014. "Information Rigidities: Comparing Average And Individual Forecasts For A Large International Panel," Working Papers 2014-001, The George Washington University, Department of Economics, Research Program on Forecasting.
  • Handle: RePEc:gwc:wpaper:2014-001
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    References listed on IDEAS

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    Cited by:

    1. Trabelsi, Emna, 2016. "Central bank transparency and the consensus forecast: What does The Economist poll of forecasters tell us?," Research in International Business and Finance, Elsevier, vol. 38(C), pages 338-359.
    2. Frédérique Bec & Raouf Boucekkine & Caroline Jardet, 2017. "Why Are Inflation Forecasts Sticky? Theory and Application to France and Germany," Working Papers halshs-01630571, HAL.
    3. 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.
    4. Joao Tovar Jalles, 2015. "How Quickly is News Incorporated in Fiscal Forecasts?," Economics Bulletin, AccessEcon, vol. 35(4), pages 2802-2812.
    5. repec:spr:empeco:v:53:y:2017:i:1:d:10.1007_s00181-016-1137-x is not listed on IDEAS
    6. Jörg Döpke & Ulrich Fritsche & Gabi Waldhof, 2017. "Theories, techniques and the formation of German business cycle forecasts: Evidence from a survey among professional forecasters," Working Papers 2017-002, The George Washington University, Department of Economics, Research Program on Forecasting.
    7. 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, Research Program on Forecasting.
    8. Frédérique BEC, 2017. "Why are inflation forecasts sticky?," THEMA Working Papers 2017-23, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise.
    9. Constantin Burgi, 2016. "What Do We Lose When We Average Expectations?," Working Papers 2016-013, The George Washington University, Department of Economics, Research Program on Forecasting.
    10. repec:spr:portec:v:16:y:2017:i:3:d:10.1007_s10258-017-0129-x is not listed on IDEAS
    11. Jordan, Steven J. & Vivian, Andrew & Wohar, Mark E., 2017. "Forecasting market returns: bagging or combining?," International Journal of Forecasting, Elsevier, vol. 33(1), pages 102-120.
    12. Jonas Dovern & Matthias Hartmann, 2017. "Forecast performance, disagreement, and heterogeneous signal-to-noise ratios," Empirical Economics, Springer, vol. 53(1), pages 63-77, August.
    13. Ericsson, Neil R., 2016. "Eliciting GDP forecasts from the FOMC’s minutes around the financial crisis," International Journal of Forecasting, Elsevier, vol. 32(2), pages 571-583.
    14. Jalles, João Tovar & Karibzhanov, Iskander & Loungani, Prakash, 2015. "Cross-country evidence on the quality of private sector fiscal forecasts," Journal of Macroeconomics, Elsevier, vol. 45(C), pages 186-201.
    15. 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.
    16. Jörg Döpke & Ulrich Fritsche & Gabi Waldhof, 2017. "Theories, techniques and the formation of German business cycle forecasts: Evidence from a survey of professional forecasters," Macroeconomics and Finance Series 201701, Hamburg University, Department Wirtschaft und Politik.
    17. Dovern, Jonas & Jannsen, Nils, 2017. "Systematic errors in growth expectations over the business cycle," International Journal of Forecasting, Elsevier, vol. 33(4), pages 760-769.
    18. Chen, Qiwei & Costantini, Mauro & Deschamps, Bruno, 2016. "How accurate are professional forecasts in Asia? Evidence from ten countries," International Journal of Forecasting, Elsevier, vol. 32(1), pages 154-167.
    19. Kenny, Geoff & Dovern, Jonas, 2017. "The long-term distribution of expected inflation in the euro area: what has changed since the great recession?," Working Paper Series 1999, European Central Bank.
    20. Frédérique Bec & Raouf Boucekkine & Caroline Jardet, 2017. "Why are inflation forecasts sticky?," Working Papers 2017-17, Center for Research in Economics and Statistics.
    21. repec:bla:obuest:v:79:y:2017:i:6:p:933-968 is not listed on IDEAS

    More about this item

    Keywords

    Rational Inattention; Aggregation Bias; Growth Forecasts; Information Rigidity; Forecast Behavior;

    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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