IDEAS home Printed from https://ideas.repec.org/a/ces/ifosdt/v68y2015i02p43-49.html
   My bibliography  Save this article

Economic Situation in 2014: Forecast and Reality

Author

Listed:
  • Wolfgang Nierhaus

Abstract

For years the Ifo Institute has critically examined the quality of its own economic forecasts. This article discusses the reasons for the differences that emerged between its forecast and reality in 2014. It also looks at the long-term average forecasting quality of Ifo’s work. Over the course of last year key framework conditions changed, which constrained economic activity on balance. In view of the asymmetrically distributed impact of events, nearly all professional forecasters overestimated actual GDP growth for 2014 in autumn 2013.

Suggested Citation

  • Wolfgang Nierhaus, 2015. "Economic Situation in 2014: Forecast and Reality," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 68(02), pages 43-49, January.
    • Handle: RePEc:ces:ifosdt:v:68:y:2015:i:02:p:43-49
    as

    Download full text from publisher

    File URL: https://www.ifo.de/DocDL/ifosd_2015_02_5.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Chatfield, Chris, 1993. "Calculating Interval Forecasts: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(2), pages 143-144, April.
    2. Wolfgang Nierhaus, 2013. "Economic Forecasts Today– Possibilities and Problems," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 66(01), pages 25-32, January.
    3. Stephen K. McNees, 1988. "How accurate are macroeconomic forecasts?," New England Economic Review, Federal Reserve Bank of Boston, issue Jul, pages 15-36.
    4. Chatfield, Chris, 1993. "Calculating Interval Forecasts," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(2), pages 121-135, April.
    5. Emile Grunberg & Franco Modigliani, 1954. "The Predictability of Social Events," Journal of Political Economy, University of Chicago Press, vol. 62, pages 465-465.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. repec:lan:wpaper:470 is not listed on IDEAS
    2. Wolfgang Nierhaus, 2017. "Economic Activity in 2016: Forecast and Reality," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 70(02), pages 72-78, January.
    3. Wolfgang Nierhaus, 2013. "Economic Forecasts Today– Possibilities and Problems," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 66(01), pages 25-32, January.
    4. Wolfgang Nierhaus, 2016. "Business Cycle 2015: Forecast and Reality," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 69(03), pages 34-40, February.
    5. repec:lan:wpaper:425 is not listed on IDEAS
    6. repec:lan:wpaper:539557 is not listed on IDEAS
    7. repec:lan:wpaper:413 is not listed on IDEAS
    8. Wolfgang Nierhaus, 2019. "Business Cycle 2018: Forecast and Reality," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 72(03), pages 22-29, February.
    9. Stefan Sauer & Klaus Wohlrabe, 2020. "ifo Handbuch der Konjunkturumfragen," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 88.
    10. Wolfgang Nierhaus, 2020. "Economic Business Cycle 2019: Forecast and Reality," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 73(01), pages 51-57, January.
    11. Wolfgang Nierhaus, 2018. "Economic Situation 2017: Forecast and Reality," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 71(03), pages 35-42, February.
    12. Fildes, Robert & Stekler, Herman, 2002. "The state of macroeconomic forecasting," Journal of Macroeconomics, Elsevier, vol. 24(4), pages 435-468, December.
    13. Berrin Aytac & S. Wu, 2013. "Characterization of demand for short life-cycle technology products," Annals of Operations Research, Springer, vol. 203(1), pages 255-277, March.
    14. Lee, Yun Shin & Scholtes, Stefan, 2014. "Empirical prediction intervals revisited," International Journal of Forecasting, Elsevier, vol. 30(2), pages 217-234.
    15. Charles, Amelie & Darne, Olivier & Kim, Jae, 2016. "Stock Return Predictability: Evaluation based on Prediction Intervals," MPRA Paper 70143, University Library of Munich, Germany.
    16. Dhaoui, Iyad, 2015. "Climat des Affaires et Compétitivité de l’Entreprise Tunisienne Après la Révolution : Analyses et Perspectives [Business Climate and Competitiveness of the Tunisian Enterprise After the Revolution:," MPRA Paper 87331, University Library of Munich, Germany.
    17. Pascual, Lorenzo & Romo, Juan & Ruiz, Esther, 2005. "Bootstrap prediction intervals for power-transformed time series," International Journal of Forecasting, Elsevier, vol. 21(2), pages 219-235.
    18. Li, Yushu & Andersson, Jonas, 2014. "A Likelihood Ratio and Markov Chain Based Method to Evaluate Density Forecasting," Discussion Papers 2014/12, Norwegian School of Economics, Department of Business and Management Science.
    19. Klaus Abberger, 2006. "Kernel smoothed prediction intervals for ARMA models," Statistical Papers, Springer, vol. 47(1), pages 1-15, January.
    20. Roberto Buizza & James W. Taylor, 2004. "A comparison of temperature density forecasts from GARCH and atmospheric models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(5), pages 337-355.
    21. Yushu Li & Jonas Andersson, 2020. "A likelihood ratio and Markov chain‐based method to evaluate density forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(1), pages 47-55, January.
    22. James W. Taylor & Derek W. Bunn, 1999. "A Quantile Regression Approach to Generating Prediction Intervals," Management Science, INFORMS, vol. 45(2), pages 225-237, February.
    23. Melard, G. & Pasteels, J. -M., 2000. "Automatic ARIMA modeling including interventions, using time series expert software," International Journal of Forecasting, Elsevier, vol. 16(4), pages 497-508.
    24. Harvey, David I. & Newbold, Paul, 2003. "The non-normality of some macroeconomic forecast errors," International Journal of Forecasting, Elsevier, vol. 19(4), pages 635-653.

    More about this item

    JEL classification:

    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ces:ifosdt:v:68:y:2015:i:02:p:43-49. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Klaus Wohlrabe (email available below). General contact details of provider: https://edirc.repec.org/data/ifooode.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.