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European economic sentiment indicator: An empirical reappraisal

Author

Listed:
  • Petar Sorić

    (Faculty of Economics and Business, University of Zagreb)

  • Ivana Lolić

    (Faculty of Economics and Business, University of Zagreb)

  • Mirjana Čižmešija

    (Faculty of Economics and Business, University of Zagreb)

Abstract

In the last five decades the European Economic Sentiment Indicator (ESI) has positioned itself as a high-quality leading indicator of overall economic activity. Relying on data from five distinct business and consumer survey sectors (industry, retail trade, services, construction and the consumer sector), ESI is conceptualized as a weighted average of the chosen 15 response balances. However, the official methodology of calculating ESI is quite flawed because of the arbitrarily chosen balance response weights. This paper proposes two alternative methods for obtaining novel weights aimed at enhancing ESI's forecasting power. Specifically, the weights are determined by minimizing the root mean square error in simple GDP forecasting regression equations; and by maximizing the correlation coefficient between ESI and GDP growth for various lead lengths (up to 12 months). Both employed methods seem to considerably increase ESI's forecasting accuracy in 26 individual European Union countries. The obtained results are quite robust across specifications.

Suggested Citation

  • Petar Sorić & Ivana Lolić & Mirjana Čižmešija, 2015. "European economic sentiment indicator: An empirical reappraisal," EFZG Working Papers Series 1505, Faculty of Economics and Business, University of Zagreb.
  • Handle: RePEc:zag:wpaper:1505
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    References listed on IDEAS

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    1. Justin Doran & Bernard Fingleton, 2014. "Economic shocks and growth: Spatio-temporal perspectives on Europe's economies in a time of crisis," Papers in Regional Science, Wiley Blackwell, vol. 93, pages 137-165, November.
    2. Thomas F. Crossley & Hamish Low & Cormac O'Dea, 2013. "Household Consumption through Recent Recessions," Fiscal Studies, Institute for Fiscal Studies, vol. 34(2), pages 203-229, June.
    3. Tommaso Proietti, 2006. "Temporal disaggregation by state space methods: Dynamic regression methods revisited," Econometrics Journal, Royal Economic Society, vol. 9(3), pages 357-372, November.
    4. Sax, Christoph & Steiner, Peter, 2013. "Temporal Disaggregation of Time Series," MPRA Paper 53389, University Library of Munich, Germany.
    5. Sarah Gelper & Christophe Croux, 2010. "On the Construction of the European Economic Sentiment Indicator," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 72(1), pages 47-62, February.
    6. Bas Aarle & Marcus Kappler, 2012. "Economic sentiment shocks and fluctuations in economic activity in the euro area and the USA," Intereconomics: Review of European Economic Policy, Springer;ZBW - Leibniz Information Centre for Economics;Centre for European Policy Studies (CEPS), vol. 47(1), pages 44-51, January.
    7. Ahec Šonje, Amina & Čeh Časni, Anita & Vizek, Maruška, 2014. "The effect of housing and stock market wealth on consumption in emerging and developed countries," Economic Systems, Elsevier, vol. 38(3), pages 433-450.
    8. Gulasekaran Rajaguru & Tilak Abeysinghe, 2004. "Quarterly real GDP estimates for China and ASEAN4 with a forecast evaluation," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(6), pages 431-447.
    9. Henry Sabrowski, 2008. "Inflation Expectation Formation of German Consumers: Rational or Adaptive?," Working Paper Series in Economics 100, University of Lüneburg, Institute of Economics.
    10. Antonides, Gerrit, 2008. "How is perceived inflation related to actual price changes in the European Union?," Journal of Economic Psychology, Elsevier, vol. 29(4), pages 417-432, August.
    11. Chow, Gregory C & Lin, An-loh, 1971. "Best Linear Unbiased Interpolation, Distribution, and Extrapolation of Time Series by Related Series," The Review of Economics and Statistics, MIT Press, vol. 53(4), pages 372-375, November.
    12. Schröder, Michael & Hüfner, Felix P., 2002. "Forecasting economic activity in Germany: how useful are sentiment indicators?," ZEW Discussion Papers 02-56, ZEW - Leibniz Centre for European Economic Research.
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    Cited by:

    1. Daniel Tomić Jurica Šimurina Luka Jovanov, 2020. "The Nexus between Economic Sentiment Indicator and Gross Domestic Product; a Panel Cointegration Analysis," Zagreb International Review of Economics and Business, Faculty of Economics and Business, University of Zagreb, vol. 23(1), pages 121-140, May.
    2. Emilian DOBRESCU, 2020. "Self-fulfillment degree of economic expectations within an integrated space: The European Union case study," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 5-32, December.

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    More about this item

    Keywords

    Business and Consumer Surveys; Economic Sentiment Indicator; Nonlinear Optimization with Constraints; Leading Indicator;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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