IDEAS home Printed from https://ideas.repec.org/a/eee/jeborg/v92y2013icp141-151.html
   My bibliography  Save this article

Can rational stubbornness explain forecast biases?

Author

Listed:
  • Deschamps, Bruno
  • Ioannidis, Christos

Abstract

This paper examines whether the rational jumpiness/stubbornness hypothesis can explain forecast biases. Using a dataset of professional GDP forecasts for the G7 countries over the period 1989–2010, we find evidence supporting the rational stubbornness hypothesis. Specifically, forecasters underreact more when large forecast revisions are highly indicative of low forecast ability. Underreaction is less likely when the size of forecast revisions is unrelated to ability. These findings are consistent with the hypothesis that forecasters choose to smooth GDP forecasts to maximize their perceived ability.

Suggested Citation

  • Deschamps, Bruno & Ioannidis, Christos, 2013. "Can rational stubbornness explain forecast biases?," Journal of Economic Behavior & Organization, Elsevier, vol. 92(C), pages 141-151.
    • Handle: RePEc:eee:jeborg:v:92:y:2013:i:c:p:141-151
    DOI: 10.1016/j.jebo.2013.05.011
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167268113001479
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jebo.2013.05.011?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Ottaviani, Marco & Sorensen, Peter Norman, 2006. "The strategy of professional forecasting," Journal of Financial Economics, Elsevier, vol. 81(2), pages 441-466, August.
    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. Clements, Michael P., 2010. "Explanations of the inconsistencies in survey respondents' forecasts," European Economic Review, Elsevier, vol. 54(4), pages 536-549, May.
    4. Fred Joutz & Michael P. Clements & Herman O. Stekler, 2007. "An evaluation of the forecasts of the federal reserve: a pooled approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(1), pages 121-136.
    5. Graham Elliott & Allan Timmermann & Ivana Komunjer, 2005. "Estimation and Testing of Forecast Rationality under Flexible Loss," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 72(4), pages 1107-1125.
    6. Qi Chen & Wei Jiang, 2006. "Analysts' Weighting of Private and Public Information," The Review of Financial Studies, Society for Financial Studies, vol. 19(1), pages 319-355.
    7. 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.
    8. Effinger, Matthias R. & Polborn, Mattias K., 2001. "Herding and anti-herding: A model of reputational differentiation," European Economic Review, Elsevier, vol. 45(3), pages 385-403, March.
    9. G. A. Christodoulakis & E. C. Mamatzakis, 2009. "Assessing the prudence of economic forecasts in the EU," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(4), pages 583-606.
    10. Ashiya, Masahiro, 2003. "Testing the rationality of Japanese GDP forecasts: the sign of forecast revision matters," Journal of Economic Behavior & Organization, Elsevier, vol. 50(2), pages 263-269, February.
    11. Ulrich K. Müller & Gebhard Kirchgässner, 2006. "Are forecasters reluctant to revise their predictions? Some German evidence," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(6), pages 401-413.
    12. Scharfstein, David S & Stein, Jeremy C, 1990. "Herd Behavior and Investment," American Economic Review, American Economic Association, vol. 80(3), pages 465-479, June.
    13. Peter Tillmann, 2011. "Reputation and Forecast Revisions: Evidence from the FOMC," MAGKS Papers on Economics 201128, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    14. Davies, Anthony & Lahiri, Kajal, 1995. "A new framework for analyzing survey forecasts using three-dimensional panel data," Journal of Econometrics, Elsevier, vol. 68(1), pages 205-227, July.
    15. David I. Harvey & Stephen J. Leybourne & Paul Newbold, 2001. "Analysis of a panel of UK macroeconomic forecasts," Econometrics Journal, Royal Economic Society, vol. 4(1), pages 37-55.
    16. David Laster & Paul Bennett & In Sun Geoum, 1999. "Rational Bias in Macroeconomic Forecasts," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 114(1), pages 293-318.
    17. Nordhaus, William D, 1987. "Forecasting Efficiency: Concepts and Applications," The Review of Economics and Statistics, MIT Press, vol. 69(4), pages 667-674, November.
    18. Steven P. Peterson, 2001. "Rational Bias In Yield Curve Forecasts," The Review of Economics and Statistics, MIT Press, vol. 83(3), pages 457-464, August.
    19. Tilman Ehrbeck & Robert Waldmann, 1996. "Why Are Professional Forecasters Biased? Agency versus Behavioral Explanations," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 111(1), pages 21-40.
    20. Prendergast, Canice & Stole, Lars, 1996. "Impetuous Youngsters and Jaded Old-Timers: Acquiring a Reputation for Learning," Journal of Political Economy, University of Chicago Press, vol. 104(6), pages 1105-1134, December.
    21. Lahiri, Kajal & Sheng, Xuguang, 2008. "Evolution of forecast disagreement in a Bayesian learning model," Journal of Econometrics, Elsevier, vol. 144(2), pages 325-340, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. Iregui, Ana María & Núñez, Héctor M. & Otero, Jesús, 2021. "Testing the efficiency of inflation and exchange rate forecast revisions in a changing economic environment," Journal of Economic Behavior & Organization, Elsevier, vol. 187(C), pages 290-314.
    3. Reslow, André, 2019. "Inefficient Use of Competitors'Forecasts?," Working Paper Series 380, Sveriges Riksbank (Central Bank of Sweden).
    4. Raffaella Giacomini, 2015. "Economic theory and forecasting: lessons from the literature," Econometrics Journal, Royal Economic Society, vol. 18(2), pages 22-41, June.
    5. Bruno Deschamps, 2015. "Are aggregate corporate earnings forecasts unbiased and efficient?," Review of Quantitative Finance and Accounting, Springer, vol. 45(4), pages 803-818, November.
    6. 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.
    7. Raffaella Giacomini, 2014. "Economic theory and forecasting: lessons from the literature," CeMMAP working papers 41/14, Institute for Fiscal Studies.
    8. Deschamps, Bruno & Ioannidis, Christos & Ka, Kook, 2020. "High-frequency credit spread information and macroeconomic forecast revision," International Journal of Forecasting, Elsevier, vol. 36(2), pages 358-372.
    9. Linnainmaa, Juhani T. & Torous, Walter & Yae, James, 2016. "Reading the tea leaves: Model uncertainty, robust forecasts, and the autocorrelation of analysts’ forecast errors," Journal of Financial Economics, Elsevier, vol. 122(1), pages 42-64.

    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. Bizer, Kilian & Meub, Lukas & Proeger, Till & Spiwoks, Markus, 2014. "Strategic coordination in forecasting: An experimental study," University of Göttingen Working Papers in Economics 195, University of Goettingen, Department of Economics.
    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. 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.
    4. Marinovic, Iván & Ottaviani, Marco & Sorensen, Peter, 2013. "Forecasters’ Objectives and Strategies," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 690-720, Elsevier.
    5. Bruno Deschamps & Christos Ioannidis, 2014. "The Efficiency of Multivariate Macroeconomic Forecasts," Manchester School, University of Manchester, vol. 82(5), pages 509-523, September.
    6. Constantin Burgi, 2016. "What Do We Lose When We Average Expectations?," Working Papers 2016-013, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    7. Reslow, André, 2019. "Inefficient Use of Competitors'Forecasts?," Working Paper Series 380, Sveriges Riksbank (Central Bank of Sweden).
    8. Papastamos, Dimitrios & Matysiak, George & Stevenson, Simon, 2015. "Assessing the accuracy and dispersion of real estate investment forecasts," International Review of Financial Analysis, Elsevier, vol. 42(C), pages 141-152.
    9. Michael P. Clements, 2014. "US Inflation Expectations and Heterogeneous Loss Functions, 1968–2010," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(1), pages 1-14, January.
    10. Clements, Michael P., 2010. "Explanations of the inconsistencies in survey respondents' forecasts," European Economic Review, Elsevier, vol. 54(4), pages 536-549, May.
    11. Lahiri, Kajal & Sheng, Xuguang, 2008. "Evolution of forecast disagreement in a Bayesian learning model," Journal of Econometrics, Elsevier, vol. 144(2), pages 325-340, June.
    12. Chang, Chia-Lin & de Bruijn, Bert & Franses, Philip Hans & McAleer, Michael, 2013. "Analyzing fixed-event forecast revisions," International Journal of Forecasting, Elsevier, vol. 29(4), pages 622-627.
    13. 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.
    14. Iregui, Ana María & Núñez, Héctor M. & Otero, Jesús, 2021. "Testing the efficiency of inflation and exchange rate forecast revisions in a changing economic environment," Journal of Economic Behavior & Organization, Elsevier, vol. 187(C), pages 290-314.
    15. Masahiro Ashiya, 2009. "Strategic bias and professional affiliations of macroeconomic forecasters," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(2), pages 120-130.
    16. Zitzewitz, Eric, 2001. "Measuring Herding and Exaggeration by Equity Analysts and Other Opinion Sellers," Research Papers 1802, Stanford University, Graduate School of Business.
    17. Peter Tillmann, 2011. "Reputation and Forecast Revisions: Evidence from the FOMC," MAGKS Papers on Economics 201128, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    18. Clements, Michael P., 2006. "Internal consistency of survey respondentsíforecasts: Evidence based on the Survey of Professional Forecasters," Economic Research Papers 269742, University of Warwick - Department of Economics.
    19. 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.
    20. Bruno Deschamps, 2015. "Are aggregate corporate earnings forecasts unbiased and efficient?," Review of Quantitative Finance and Accounting, Springer, vol. 45(4), pages 803-818, November.

    More about this item

    Keywords

    Forecast efficiency; GDP; Forecasting; Underreaction;
    All these keywords.

    JEL classification:

    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

    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:eee:jeborg:v:92:y:2013:i:c:p:141-151. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/jebo .

    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.