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Coincident and leading indicators for the euro area: A frequency band approach

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  • Rua, Antonio
  • Nunes, Luis C.

Abstract

In the context of a common monetary policy, tracking euro area economic developments becomes essential. The aim of this paper is to build monthly coincident and leading composite indicators for the euro area business cycle. However, instead of looking at the overall comovement between the variables as it is standard in the literature, we show how one can resort to both time and frequency domain analysis to achieve additional insight about their relationship. We find that, in general, the lead/lag properties of economic indicators depend on the cycles periodicity. Following a frequency band approach, we take advantage of this in the construction of the coincident and leading composite indicators. The resulting indicators are analysed and a comparison with other composite indicators proposed in the literature is made.
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  • Rua, Antonio & Nunes, Luis C., 2005. "Coincident and leading indicators for the euro area: A frequency band approach," International Journal of Forecasting, Elsevier, vol. 21(3), pages 503-523.
    • Handle: RePEc:eee:intfor:v:21:y:2005:i:3:p:503-523
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    Cited by:

    1. Ard den Reijer, 2006. "The Dutch business cycle: which indicators should we monitor?," DNB Working Papers 100, Netherlands Central Bank, Research Department.
    2. Jens J. Krüger, 2021. "A Wavelet Evaluation of Some Leading Business Cycle Indicators for the German Economy," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 17(3), pages 293-319, December.
    3. António Rua, 2012. "Wavelets in economics," Economic Bulletin and Financial Stability Report Articles and Banco de Portugal Economic Studies, Banco de Portugal, Economics and Research Department.
    4. Gazi Salah Uddin & Mohamed Arouri & Aviral Kumar Tiwari, 2014. "Co-movements between Germany and International Stock Markets: Some New Evidence from DCC-GARCH and Wavelet Approaches," Working Papers 2014-143, Department of Research, Ipag Business School.
    5. de Carvalho, Miguel & Rua, António, 2017. "Real-time nowcasting the US output gap: Singular spectrum analysis at work," International Journal of Forecasting, Elsevier, vol. 33(1), pages 185-198.
    6. 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.
    7. Cravo, Túlio A., 2011. "Are small employers more cyclically sensitive? Evidence from Brazil," Journal of Macroeconomics, Elsevier, vol. 33(4), pages 754-769.
    8. Rua, António, 2010. "Measuring comovement in the time-frequency space," Journal of Macroeconomics, Elsevier, vol. 32(2), pages 685-691, June.
    9. Abberger, Klaus & Graff, Michael & Siliverstovs, Boriss & Sturm, Jan-Egbert, 2018. "Using rule-based updating procedures to improve the performance of composite indicators," Economic Modelling, Elsevier, vol. 68(C), pages 127-144.
    10. Éric Dubois, 2006. "Présentation générale," Économie et Prévision, Programme National Persée, vol. 172(1), pages 1-9.
    11. repec:ipg:wpaper:2014-441 is not listed on IDEAS
    12. António Rua & Artur Silva Lopes, 2015. "Cohesion within the euro area and the US: A wavelet-based view," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2014(2), pages 63-76.
    13. Soh, Ann-Ni, 2020. "A Review on the Leading Indicator Approach towards Economic Forecasting," MPRA Paper 103854, University Library of Munich, Germany.
    14. Lemmens, A. & Croux, C. & Dekimpe, M.G., 2007. "Consumer confidence in Europe : United in diversity," Other publications TiSEM ea8c3268-2c0b-4fcc-9d4a-6, Tilburg University, School of Economics and Management.
    15. Miljkovic, Dragan & Vatsa, Puneet, 2023. "On the linkages between energy and agricultural commodity prices: A dynamic time warping analysis," International Review of Financial Analysis, Elsevier, vol. 90(C).
    16. Rua, António, 2017. "A wavelet-based multivariate multiscale approach for forecasting," International Journal of Forecasting, Elsevier, vol. 33(3), pages 581-590.
    17. Ftiti, Zied & Guesmi, Khaled & Abid, Ilyes, 2016. "Oil price and stock market co-movement: What can we learn from time-scale approaches?," International Review of Financial Analysis, Elsevier, vol. 46(C), pages 266-280.
    18. Gallegati, Marco & Delli Gatti, Domenico, 2018. "Macrofinancial imbalances in historical perspective: A global crisis index," Journal of Economic Dynamics and Control, Elsevier, vol. 91(C), pages 190-205.
    19. Krüger, Jens J., 2024. "A Wavelet Evaluation of Some Leading Business Cycle Indicators for the German Economy," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 149438, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    20. Junyi Shi, 2020. "Re-Measurement Of Short-Term International Capital Flows And Its Application: Evidence From China," The Singapore Economic Review (SER), World Scientific Publishing Co. Pte. Ltd., vol. 65(06), pages 1645-1665, December.
    21. Kosei Fukuda, 2008. "Differentiating between business cycles and growth cycles: evidence from 15 developed countries," Applied Economics, Taylor & Francis Journals, vol. 40(7), pages 875-883.

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