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Wavelet variance and correlation analyses of output in G7 countries

Author

Listed:
  • Marco Gallegati

    (DEA, Università Politecnica delle Marche, Italy)

  • Mauro Gallegati

    (DEA, Università Politecnica delle Marche, Italy)

Abstract
In this paper we apply the wavelets methodology to the analysis of the industrial production index of the G-7 countries between 1961:1-2005:5. The analysis is performed using a multi-scaling approach which decomposes the variance of the industrial production index and the covariance between the industrial production indices of two countries on a scale-by-scale basis through a non-orthogonal variant of the classical discrete wavelet transform, i.e. the maximal overlap discrete wavelet transform (MODWT). Wavelet variance analysis does not provide evidence of an international patterns of moderation in output volatility, as the moderation of output volatility occurred after the early eighties is confirmed only for the Euro-area countries plus Japan. Moreover, wavelet correlation analysis different correlation patterns at the different time-scale components and, that, with some exceptions, the linkages between countries are mostly significant only at the business cycle time scales, with the strongest relationships between the Anglo countries (particularly Canada and US), France and Germany, Japan and the Euro- zone countries, with Italy displaying the closest links with France.

Suggested Citation

  • Marco Gallegati & Mauro Gallegati, 2005. "Wavelet variance and correlation analyses of output in G7 countries," Macroeconomics 0512017, University Library of Munich, Germany.
    • Handle: RePEc:wpa:wuwpma:0512017
    Note: Type of Document - pdf; pages: 21
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    File URL: https://econwpa.ub.uni-muenchen.de/econ-wp/mac/papers/0512/0512017.pdf
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    References listed on IDEAS

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    2. Chakrabarty, Anindya & De, Anupam & Gunasekaran, Angappa & Dubey, Rameshwar, 2015. "Investment horizon heterogeneity and wavelet: Overview and further research directions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 429(C), pages 45-61.
    3. Ijaz Younis & Cheng Longsheng & Muhammad Farhan Basheer & Ahmed Shafique Joyo, 2020. "Stock market comovements among Asian emerging economies: A wavelet-based approach," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-23, October.
    4. João Martins, 2022. "Bond Yields Movement Similarities and Synchronization in the G7: A Time–Frequency Analysis," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 18(2), pages 189-214, July.
    5. Atilla Cifter & Alper Ozun, 2008. "Multiscale Systematic Risk: an Application on the ISE-30," Istanbul Stock Exchange Review, Research and Business Development Department, Borsa Istanbul, vol. 10(38), pages 1-24.

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    More about this item

    Keywords

    time-scale decomposition analysis; wavelets; business cycle fluctuations;
    All these keywords.

    JEL classification:

    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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