IDEAS home Printed from https://ideas.repec.org/p/zag/wpaper/1505.html
   My bibliography  Save this paper

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
    as

    Download full text from publisher

    File URL: http://web.efzg.hr/repec/pdf/Clanak%2015-05.pdf
    File Function: First version, 2015
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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. 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.
    10. 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.
    11. 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.
    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. 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.

    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. 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.
    2. Vladimir Boyko & Nadezhda Kislyak & Mikhail Nikitin & Oleg Oborin, 2020. "Methods for Estimating the Gross Regional Product Leading Indicator," Russian Journal of Money and Finance, Bank of Russia, vol. 79(3), pages 3-29, September.
    3. Luke Mosley & Idris Eckley & Alex Gibberd, 2021. "Sparse Temporal Disaggregation," Papers 2108.05783, arXiv.org.
    4. Enrique M. Quilis, 2018. "Temporal disaggregation of economic time series: The view from the trenches," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 72(4), pages 447-470, November.
    5. 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.
    6. Proietti, Tommaso, 2008. "Estimation of Common Factors under Cross-Sectional and Temporal Aggregation Constraints: Nowcasting Monthly GDP and its Main Components," MPRA Paper 6860, University Library of Munich, Germany.
    7. Laura Bisio & Filippo Moauro, 2018. "Temporal disaggregation by dynamic regressions: Recent developments in Italian quarterly national accounts," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 72(4), pages 471-494, November.
    8. Tommaso Proietti, 2011. "Multivariate temporal disaggregation with cross-sectional constraints," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(7), pages 1455-1466, June.
    9. Travaglini, Guido, 2010. "Supervised Principal Components and Factor Instrumental Variables. An Application to Violent CrimeTrends in the US, 1982-2005," MPRA Paper 22077, University Library of Munich, Germany.
    10. Lorenza Rossi & Emilio Zanetti Chini, 2016. "Firms’ Dynamics and Business Cycle: New Disaggregated Data," DEM Working Papers Series 123, University of Pavia, Department of Economics and Management.
    11. Proietti, Tommaso, 2008. "Band spectral estimation for signal extraction," Economic Modelling, Elsevier, vol. 25(1), pages 54-69, January.
    12. Huang, Yu-Lieh, 2012. "Measuring business cycles: A temporal disaggregation model with regime switching," Economic Modelling, Elsevier, vol. 29(2), pages 283-290.
    13. Chengsi Zhang, 2013. "Has Chinese economy become more stable?," Journal of the Asia Pacific Economy, Taylor & Francis Journals, vol. 18(1), pages 133-148.
    14. Zhang, Chengsi & Murasawa, Yasutomo, 2012. "Multivariate model-based gap measures and a new Phillips curve for China," China Economic Review, Elsevier, vol. 23(1), pages 60-70.
    15. Bruno Chiarini & Elisabetta Marzano & Friedrich Schneider, 2013. "Tax rates and tax evasion: an empirical analysis of the long-run aspects in Italy," European Journal of Law and Economics, Springer, vol. 35(2), pages 273-293, April.
    16. Massimiliano Marcellino, 2007. "Pooling‐Based Data Interpolation and Backdating," Journal of Time Series Analysis, Wiley Blackwell, vol. 28(1), pages 53-71, January.
    17. Raffaella Basile & Bruno Chiarini & Elisabetta Marzano, 2011. "Can we Rely upon Fiscal Policy Estimates in Countries with Unreported Production of 15 Per Cent (or more) of GDP?," CESifo Working Paper Series 3521, CESifo.
    18. Baltagi, Badi H. & Fingleton, Bernard & Pirotte, Alain, 2019. "A time-space dynamic panel data model with spatial moving average errors," Regional Science and Urban Economics, Elsevier, vol. 76(C), pages 13-31.
    19. Valter Giacinto & Libero Monteforte & Andrea Filippone & Francesco Montaruli & Tiziano Ropele, 2021. "ITER: A Quarterly Indicator of Regional Economic Activity in Italy," Italian Economic Journal: A Continuation of Rivista Italiana degli Economisti and Giornale degli Economisti, Springer;Società Italiana degli Economisti (Italian Economic Association), vol. 7(1), pages 129-147, March.
    20. Eric Girardin, 2005. "Growth-cycle features of East Asian countries: are they similar?," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 10(2), pages 143-156.

    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

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:zag:wpaper:1505. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: . General contact details of provider: https://edirc.repec.org/data/fefzghr.html .

    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: WPS The email address of this maintainer does not seem to be valid anymore. Please ask WPS to update the entry or send us the correct address (email available below). General contact details of provider: https://edirc.repec.org/data/fefzghr.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.