IDEAS home Printed from https://ideas.repec.org/p/zbw/rwidps/51.html
   My bibliography  Save this paper

Improving Business Cycle Forecasts' Accuracy - What Can We Learn from Past Errors?

Author

Listed:
  • Döhrn, Roland

Abstract

This paper addresses the question whether forecasters could have been able to produce better forecasts by using the available information more efficiently (informational efficiency of forecast). It is tested whether forecast errors covariate with indicators such as survey results, monetary data, business cycle indicators, or financial data. Because of the short sampling period and data problems, a non parametric ranked sign test is applied. The analysis is carried out for GDP and its main components. The study differentiates between two types of errors: Type I error occurs when forecasters neglect the information provided by an indicator.As type II error a situation is labelled in which forecasters have given too much weight to an indicator. In a number of cases forecast errors and the indicators are correlated, though mostly at a rather low level of significance. In most cases type I errors have been found. Additional tests reveal that there is little evidence of institution specific as well as forecast horizon specific effects. In many cases, co-variations found for GDP are not refected in one of the expenditure side components et vice versa.

Suggested Citation

  • Döhrn, Roland, 2006. "Improving Business Cycle Forecasts' Accuracy - What Can We Learn from Past Errors?," RWI Discussion Papers 51, RWI - Leibniz-Institut für Wirtschaftsforschung.
    • Handle: RePEc:zbw:rwidps:51
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/18602/1/DP_06_051.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Filip Keereman, 1999. "The track record of the Commission forecasts," European Economy - Economic Papers 2008 - 2015 137, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.
    2. Holden, K & Peel, D A, 1990. "On Testing for Unbiasedness and Efficiency of Forecasts," The Manchester School of Economic & Social Studies, University of Manchester, vol. 58(2), pages 120-127, June.
    3. Oller, Lars-Erik & Barot, Bharat, 2000. "The accuracy of European growth and inflation forecasts," International Journal of Forecasting, Elsevier, vol. 16(3), pages 293-315.
    4. Campbell, Bryan & Dufour, Jean-Marie, 1995. "Exact Nonparametric Orthogonality and Random Walk Tests," The Review of Economics and Statistics, MIT Press, vol. 77(1), pages 1-16, February.
    5. Gebhardt Kirschgässner & Marcel Savioz, 2001. "Monetary Policy and Forecasts for Real GDP Growth: An Empirical Investigation for the Federal Republic of Germany," German Economic Review, Verein für Socialpolitik, vol. 2(4), pages 339-365, November.
    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. Katharina Glass, 2018. "Predictability of Euro Area Revisions," Macroeconomics and Finance Series 201801, University of Hamburg, Department of Socioeconomics.
    2. Igor Lebrun, 2007. "Working paper 08-07 - An accuracy assessment of FPB’s medium-term projections," Working Papers 0708, Federal Planning Bureau, Belgium.

    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:zbw:rwidps:0051 is not listed on IDEAS
    2. Roland Döhrn, 2006. "Improving Business Cycle Forecasts’ Accuracy - What Can We Learn from Past Errors?," RWI Discussion Papers 0051, Rheinisch-Westfälisches Institut für Wirtschaftsforschung.
    3. Yoichi Tsuchiya, 2021. "Thirty‐year assessment of Asian Development Bank's forecasts," Asian-Pacific Economic Literature, The Crawford School, The Australian National University, vol. 35(2), pages 18-40, November.
    4. Artur C. B. Da Silva Lopes, 1998. "On the 'restricted cointegration test' as a test of the rational expectations hypothesis," Applied Economics, Taylor & Francis Journals, vol. 30(2), pages 269-278, February.
    5. Dimitrios Papastamos & Fotis Mouzakis & Simon Stevenson, 2014. "Rationality and Momentum in Real Estate Investment Forecasts," Real Estate & Planning Working Papers rep-wp2014-07, Henley Business School, University of Reading.
    6. Gavin, William T. & Mandal, Rachel J., 2003. "Evaluating FOMC forecasts," International Journal of Forecasting, Elsevier, vol. 19(4), pages 655-667.
    7. Teresa Leal & Javier J. Pérez & Mika Tujula & Jean-Pierre Vidal, 2008. "Fiscal Forecasting: Lessons from the Literature and Challenges," Fiscal Studies, Institute for Fiscal Studies, vol. 29(3), pages 347-386, September.
    8. Loungani, Prakash, 2001. "How accurate are private sector forecasts? Cross-country evidence from consensus forecasts of output growth," International Journal of Forecasting, Elsevier, vol. 17(3), pages 419-432.
    9. Ashiya, Masahiro, 2007. "Forecast accuracy of the Japanese government: Its year-ahead GDP forecast is too optimistic," Japan and the World Economy, Elsevier, vol. 19(1), pages 68-85, January.
    10. Ricardo Sousa & James Yetman, 2016. "Inflation expectations and monetary policy," BIS Papers chapters, in: Bank for International Settlements (ed.), Inflation mechanisms, expectations and monetary policy, volume 89, pages 41-67, Bank for International Settlements.
    11. repec:onb:oenbwp:y::i:151:b:1 is not listed on IDEAS
    12. Ullrich Heilemann & Karsten Müller, 2018. "Wenig Unterschiede – Zur Treffsicherheit Internationaler Prognosen und Prognostiker [Few differences—on the accuracy of international forecasts and forecaster]," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 12(3), pages 195-233, December.
    13. Ulrich Fritsche & Jörg Döpke, 2006. "Treffgenauigkeit, Rationalität und Streuung von Konjunkturprognosen für Deutschland," Vierteljahrshefte zur Wirtschaftsforschung / Quarterly Journal of Economic Research, DIW Berlin, German Institute for Economic Research, vol. 75(2), pages 34-53.
    14. Ines Fortin & Sebastian P. Koch & Klaus Weyerstrass, 2020. "Evaluation of economic forecasts for Austria," Empirical Economics, Springer, vol. 58(1), pages 107-137, January.
    15. Vuchelen, Jef & Gutierrez, Maria-Isabel, 2005. "A direct test of the information content of the OECD growth forecasts," International Journal of Forecasting, Elsevier, vol. 21(1), pages 103-117.
    16. Behrens, Christoph, 2019. "Evaluating the Joint Efficiency of German Trade Forecasts. A nonparametric multivariate approach," Working Papers 9, German Research Foundation's Priority Programme 1859 "Experience and Expectation. Historical Foundations of Economic Behaviour", Humboldt University Berlin.
    17. Fildes, Robert & Stekler, Herman, 2002. "The state of macroeconomic forecasting," Journal of Macroeconomics, Elsevier, vol. 24(4), pages 435-468, December.
    18. Vasconcelos de Deus, Joseph David Barroso & de Mendonça, Helder Ferreira, 2017. "Fiscal forecasting performance in an emerging economy: An empirical assessment of Brazil," Economic Systems, Elsevier, vol. 41(3), pages 408-419.
    19. Laura Carabotta, 2014. "Which Agency and Which Period is The Best? Analyzing National and International Fiscal Forecasts in Italy," International Journal of Economic Sciences, Prague University of Economics and Business, vol. 2014(1), pages 27-46.
    20. Behrens, Christoph, 2020. "German trade forecasts since 1970: An evaluation using the panel dimension," Working Papers 26, German Research Foundation's Priority Programme 1859 "Experience and Expectation. Historical Foundations of Economic Behaviour", Humboldt University Berlin.
    21. Leal Linares, Teresa & Pérez García, Javier J., 2011. "Análisis de las desviaciones presupuestarias aplicado al caso del presupuesto del Estado/The Performance of the Budgetary Target of the Central Government in Spain," Estudios de Economia Aplicada, Estudios de Economia Aplicada, vol. 29, pages 909(14á.)-9, Diciembre.
    22. Behrens, Christoph & Pierdzioch, Christian & Risse, Marian, 2018. "Testing the optimality of inflation forecasts under flexible loss with random forests," Economic Modelling, Elsevier, vol. 72(C), pages 270-277.

    More about this item

    Keywords

    Short term forecast; Forecast evaluation; informational efficiency;
    All these keywords.

    JEL classification:

    • C42 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Survey Methods
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

    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:zbw:rwidps:51. 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: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://edirc.repec.org/data/rwiesde.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.