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An assessment of the EU growth forecasts under asymmetric preferences

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  • George A. Christodoulakis

    (Manchester Business School, University of Manchester, Booth Street West, Manchester M15 6PB, UK)

  • Emmanuel C. Mamatzakis

    (Department of Economics, University of Macedonia, Egnatia 156, Thessaloniki 540 06, Greece)

Abstract

EU Commission forecasts are used as a benchmark within the framework of the Stability and Growth Pact, aimed at providing a prudential view of economic outlook, especially for member states in an Excessive Deficit Procedure. Following Elliott et al. (2005), we assess whether there exist asymmetries in the loss preference of the Commission's GDP growth forecasts from 1969 to 2004. Our empirical evidence is robust across information sets and reveals that the loss preferences tend to show some variation in terms of asymmetry across member states. Given certain conditions concerning the time horizon of forecasts and the functional form of the loss preferences, the evidence further reveals that the Commission forecasting exercise could be subject to caveats. Copyright © 2008 John Wiley & Sons, Ltd.

Suggested Citation

  • George A. Christodoulakis & Emmanuel C. Mamatzakis, 2008. "An assessment of the EU growth forecasts under asymmetric preferences," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(6), pages 483-492.
    • Handle: RePEc:jof:jforec:v:27:y:2008:i:6:p:483-492
    DOI: 10.1002/for.1073
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    References listed on IDEAS

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    7. Graham Elliott & Ivana Komunjer & Allan Timmermann, 2008. "Biases in Macroeconomic Forecasts: Irrationality or Asymmetric Loss?," Journal of the European Economic Association, MIT Press, vol. 6(1), pages 122-157, March.
    8. Mr. Michael J. Artis, 1996. "How Accurate Are the Imf's Short-Term Forecasts? Another Examination of the World Economic Outlook," IMF Working Papers 1996/089, International Monetary Fund.
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    Cited by:

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    3. 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.
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    6. Tsuchiya, Yoichi, 2023. "Assessing the World Bank’s growth forecasts," Economic Analysis and Policy, Elsevier, vol. 77(C), pages 64-84.
    7. Demetrescu, Matei & Hacıoğlu Hoke, Sinem, 2019. "Predictive regressions under asymmetric loss: Factor augmentation and model selection," International Journal of Forecasting, Elsevier, vol. 35(1), pages 80-99.
    8. Christoph Behrens, 2019. "A Nonparametric Evaluation of the Optimality of German Export and Import Growth Forecasts under Flexible Loss," Economies, MDPI, vol. 7(3), pages 1-23, September.
    9. Rülke, Jan-Christoph & Pierdzioch, Christian, 2014. "Government Forecasts of Budget Balances Under Asymmetric Loss: International Evidence," VfS Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100317, Verein für Socialpolitik / German Economic Association.
    10. Siddhartha S. Bora & Ani L. Katchova & Todd H. Kuethe, 2021. "The Rationality of USDA Forecasts under Multivariate Asymmetric Loss," American Journal of Agricultural Economics, John Wiley & Sons, vol. 103(3), pages 1006-1033, May.
    11. Jörg Döpke & Ulrich Fritsche & Boriss Siliverstovs, 2010. "Evaluating German business cycle forecasts under an asymmetric loss function," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2010(1), pages 1-18.
    12. Yoichi Tsuchiya, 2022. "Evaluating plant managers’ production plans over business cycles: asymmetric loss and rationality," SN Business & Economics, Springer, vol. 2(8), pages 1-29, August.
    13. Tsuchiya, Yoichi, 2016. "Assessing macroeconomic forecasts for Japan under an asymmetric loss function," International Journal of Forecasting, Elsevier, vol. 32(2), pages 233-242.
    14. Fildes, Robert, 2015. "Forecasters and rationality—A comment on Fritsche et al., Forecasting the Brazilian Real and Mexican Peso: Asymmetric loss, forecast rationality and forecaster herding," International Journal of Forecasting, Elsevier, vol. 31(1), pages 140-143.
    15. Emmanuel C. Mamatzakis & Mike G. Tsionas, 2020. "Revealing forecaster's preferences: A Bayesian multivariate loss function approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(3), pages 412-437, April.
    16. Pierdzioch, Christian & Risse, Marian & Rohloff, Sebastian, 2014. "The international business cycle and gold-price fluctuations," The Quarterly Review of Economics and Finance, Elsevier, vol. 54(2), pages 292-305.
    17. Matei Demetrescu & Christoph Roling & Anna Titova, 2021. "Reevaluating the prudence of economic forecasts in the EU: The role of instrument persistence," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(1), pages 151-161, January.
    18. Yoichi Tsuchiya, 2024. "Conservatism and information rigidity of the European Bank for Reconstruction and Development's growth forecast: Quarter‐century assessment," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(5), pages 1399-1421, August.
    19. Giovannelli, Alessandro & Pericoli, Filippo Maria, 2020. "Are GDP forecasts optimal? Evidence on European countries," International Journal of Forecasting, Elsevier, vol. 36(3), pages 963-973.

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