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Forecast Evaluation of European Commission Survey Indicators


  • Christian Gayer



This study examines the contribution of several survey indicators published by the European Commission to forecasting overall economic activity in the euro area. It entails a quantitative evaluation of the information content of seven composite indicators with regard to GDP growth. A preliminary analysis looks at the stationarity and correlation properties of the variables. Based on bivariate VAR-models and the notion of forecast improvement, the methodological approach is two-fold: In a first step, the focussed relations are studied from an ex post perspective. Employing standard and individual Granger-causality tests, an initial assessment of the mean predictive content of the indicators is provided.

Suggested Citation

  • Christian Gayer, 2006. "Forecast Evaluation of European Commission Survey Indicators," Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2005(2), pages 157-183.
  • Handle: RePEc:oec:stdkaa:5l9vc43c09ms

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    References listed on IDEAS

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    1. repec:spr:jbuscr:v:12:y:2016:i:1:d:10.1007_s41549-016-0002-5 is not listed on IDEAS
    2. repec:spr:jbuscr:v:13:y:2017:i:2:d:10.1007_s41549-017-0020-y is not listed on IDEAS
    3. Petar Sorić & Ivana Lolić & Mirjana Čižmešija, 2016. "European economic sentiment indicator: an empirical reappraisal," Quality & Quantity: International Journal of Methodology, Springer, vol. 50(5), pages 2025-2054, September.
    4. Christian Seiler, 2012. "On the Robustness of the Balance Statistics with respect to Nonresponse," ifo Working Paper Series 126, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    5. Christian Seiler, 2013. "Nonresponse in Business Tendency Surveys: Theoretical Discourse and Empirical Evidence," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 52, November.
    6. Robert Lehmann & Antje Weyh, 2016. "Forecasting Employment in Europe: Are Survey Results Helpful?," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 12(1), pages 81-117, September.
    7. Robert Lehmann, 2015. "Survey-based indicators vs. hard data: What improves export forecasts in Europe?," ERSA conference papers ersa15p756, European Regional Science Association.
    8. Luc Dresse & Christophe Van Nieuwenhuyze, 2008. "Do survey indicators let us see the business cycle ? A frequency decomposition," Working Paper Research 131, National Bank of Belgium.
    9. Hyejung Moon & Jungick Lee, 2013. "Forecast evaluation of economic sentiment indicator for the Korean economy," IFC Bulletins chapters,in: Bank for International Settlements (ed.), Proceedings of the Sixth IFC Conference on "Statistical issues and activities in a changing environment", Basel, 28-29 August 2012., volume 36, pages 180-190 Bank for International Settlements.
    10. Michael Graff, 2006. "Ein multisektoraler Sammelindikator für die Schweizer Konjunktur," Swiss Journal of Economics and Statistics (SJES), Swiss Society of Economics and Statistics (SSES), vol. 142(IV), pages 529-577, December.
    11. Kieran Mc Morrow & Werner Roeger & Valerie Vandermeulen, 2017. "Evaluating Medium Term Forecasting Methods and their Implications for EU Output Gap Calculations," European Economy - Discussion Papers 2015 - 070, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.
    12. Christian Seiler, 2015. "On the robustness of balance statistics with respect to nonresponse," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2014(2), pages 45-62.


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