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Making leading indicators more leading: A wavelet-based method for the construction of composite leading indexes

Author

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  • Marco Gallegati
Abstract
This paper proposes a novel wavelet-based approach for constructing composite indicators. The wavelet-based methodology exploits the ability of wavelet analysis to analyse the relationships between variables on a scale-by-scale, rather than aggregate, basis. A wavelet-based index which combines several scale-based subindexes is constructed by using a scale-by-scale selection of the components included in the OECD composite leading indicator (CLI) for the US. The comparison with the CLI and its derived measures indicate that the wavelet-based composite index tends to provide early signals of business cycle turning points well in advance of the OECD CLI. Moreover we find that the reliability of the signals tends to increase considerably when the sub-index obtained from the time scale components corresponding to minor cycles, that is, 2-4 years, is removed from the overall wavelet-based index. Keywords: wavelets; composite leading indicators; early warning signals JEL classification: C1; C3; C5; E3

Suggested Citation

  • Marco Gallegati, 2014. "Making leading indicators more leading: A wavelet-based method for the construction of composite leading indexes," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2014(1), pages 1-21.
    • Handle: RePEc:oec:stdkab:5jxx56gqmhf1
    DOI: 10.1787/jbcma-2014-5jxx56gqmhf1
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    Citations

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    Cited by:

    1. 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.
    2. Mei-Teing Chong & Chin-Hong Puah & Shazali Abu Mansor, 2018. "Oil Price Dynamics Forecasting: An Indicator-Pivoted Paradigm," International Journal of Energy Economics and Policy, Econjournals, vol. 8(3), pages 307-311.
    3. Gallegati, Marco & Giri, Federico & Palestrini, Antonio, 2019. "DSGE model with financial frictions over subsets of business cycle frequencies," Journal of Economic Dynamics and Control, Elsevier, vol. 100(C), pages 152-163.
    4. Rua, António, 2017. "A wavelet-based multivariate multiscale approach for forecasting," International Journal of Forecasting, Elsevier, vol. 33(3), pages 581-590.
    5. 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).
    6. 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.

    More about this item

    Keywords

    wavelets; composite leading indicators; early warning signals jel classification: c1; c3; c5; e3;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • E3 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles

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