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A Wavelet Method for Detecting Turning Points in the Business Cycle

Author

Listed:
  • C. Colther

    (Universidad Austral de Chile)

  • J. L. Rojo

    (Universidad de Valladolid)

  • R. Hornero

    (Universidad de Valladolid)

Abstract

This paper presents a new method for detecting turning points in business cycles using the discrete wavelet transform. A methodology is proposed to select the ideal wavelet function and optimize the identification method. We illustrate the method by analyzing the 1957–2021 United States business cycle. We compare the effectiveness of wavelet functions with the classical detection technique usually employed for this type of analysis.

Suggested Citation

  • C. Colther & J. L. Rojo & R. Hornero, 2022. "A Wavelet Method for Detecting Turning Points in the Business Cycle," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 18(2), pages 171-187, July.
    • Handle: RePEc:spr:jbuscr:v:18:y:2022:i:2:d:10.1007_s41549-022-00072-y
    DOI: 10.1007/s41549-022-00072-y
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    References listed on IDEAS

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    More about this item

    Keywords

    Turning points; Business cycle; Wavelet functions; Method of detection; Wavelet coefficient;
    All these keywords.

    JEL classification:

    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • F44 - International Economics - - Macroeconomic Aspects of International Trade and Finance - - - International Business Cycles
    • C65 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Miscellaneous Mathematical Tools
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

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