A Wavelet Method for Detecting Turning Points in the Business Cycle

C. Colther (), J. L. Rojo () and R. Hornero ()
Additional contact information
C. Colther: Universidad Austral de Chile
J. L. Rojo: Universidad de Valladolid
R. Hornero: Universidad de Valladolid

Journal of Business Cycle Research, 2022, vol. 18, issue 2, No 3, 187 pages

Abstract: 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.

Keywords: Turning points; Business cycle; Wavelet functions; Method of detection; Wavelet coefficient (search for similar items in EconPapers)
JEL-codes: C14 C65 E32 F44 (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s41549-022-00072-y

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