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Wavelet: a new tool for business cycle analysis

Author

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  • Sharif Md. Raihan
  • Yi Wen
  • Bing Zeng

Abstract

One basic problem in business-cycle studies is how to deal with nonstationary time series. The market economy is an evolutionary system. Economic time series therefore contain stochastic components that are necessarily time dependent. Traditional methods of business cycle analysis, such as the correlation analysis and the spectral analysis, cannot capture such historical information because they do not take the time-varying characteristics of the business cycles into consideration. In this paper, we introduce and apply a new technique to the studies of the business cycle: the wavelet-based time-frequency analysis that has recently been developed in the field of signal processing. This new method allows us to characterize and understand not only the timing of shocks that trigger the business cycle, but also situations where the frequency of the business cycle shifts in time. Our empirical analyses show that 1973 marks a new era for the evolution of the business cycle.

Suggested Citation

  • Sharif Md. Raihan & Yi Wen & Bing Zeng, 2005. "Wavelet: a new tool for business cycle analysis," Working Papers 2005-050, Federal Reserve Bank of St. Louis.
    • Handle: RePEc:fip:fedlwp:2005-050
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    Cited by:

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    2. Luís Francisco Aguiar-Conraria & Maria Joana Soares, 2007. "Using cross-wavelets to decompose the time-frequency relation between oil and the macroeconomy," NIPE Working Papers 16/2007, NIPE - Universidade do Minho.
    3. Hua, Jia-Chen & Roy, Sukesh & McCauley, Joseph L. & Gunaratne, Gemunu H., 2016. "Using dynamic mode decomposition to extract cyclic behavior in the stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 448(C), pages 172-180.
    4. Cifter, Atilla & Yilmazer, Sait & Cifter, Elif, 2009. "Analysis of sectoral credit default cycle dependency with wavelet networks: Evidence from Turkey," Economic Modelling, Elsevier, vol. 26(6), pages 1382-1388, November.
    5. Saeed, Asif & Chaudhry, Sajid M. & Arif, Ahmed & Ahmed, Rizwan, 2023. "Spillover of energy commodities and inflation in G7 plus Chinese economies," Energy Economics, Elsevier, vol. 127(PA).
    6. Qing Pei & David D Zhang & Guodong Li & Harry F Lee, 2015. "Climate Change and the Macroeconomic Structure in Pre-Industrial Europe: New Evidence from Wavelet Analysis," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-17, June.
    7. Chai, Soo H. & Lim, Joon S., 2016. "Forecasting business cycle with chaotic time series based on neural network with weighted fuzzy membership functions," Chaos, Solitons & Fractals, Elsevier, vol. 90(C), pages 118-126.
    8. Rua, António & Nunes, Luis C., 2012. "A wavelet-based assessment of market risk: The emerging markets case," The Quarterly Review of Economics and Finance, Elsevier, vol. 52(1), pages 84-92.
    9. Aviral Kumar Tiwari & Emmanuel Joel Aikins Abakah & Luis A. Gil-Alana & Moses Kenneth Abakah, 2021. "Inflation Co-Movement Dynamics: A Cross-Country Investigation Using a Continuous Wavelet Approach," JRFM, MDPI, vol. 14(12), pages 1-43, December.
    10. Abid, Fathi & Kaffel, Bilel, 2018. "Time–frequency wavelet analysis of the interrelationship between the global macro assets and the fear indexes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 1028-1045.
    11. Andrieș, Alin Marius & Ihnatov, Iulian & Tiwari, Aviral Kumar, 2014. "Analyzing time–frequency relationship between interest rate, stock price and exchange rate through continuous wavelet," Economic Modelling, Elsevier, vol. 41(C), pages 227-238.
    12. Anand, B. & Paul, Sunil & Ramachandran, M., 2014. "Volatility Spillover between Oil and Stock Market Returns," Indian Economic Review, Department of Economics, Delhi School of Economics, vol. 49(1), pages 37-56.
    13. Bilgili, Faik, 2015. "Business cycle co-movements between renewables consumption and industrial production: A continuous wavelet coherence approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 325-332.
    14. Aguiar-Conraria, LuI´s & Joana Soares, Maria, 2011. "Business cycle synchronization and the Euro: A wavelet analysis," Journal of Macroeconomics, Elsevier, vol. 33(3), pages 477-489, September.
    15. Tonn, Victor Lux & Li, H.C. & McCarthy, Joseph, 2010. "Wavelet domain correlation between the futures prices of natural gas and oil," The Quarterly Review of Economics and Finance, Elsevier, vol. 50(4), pages 408-414, November.
    16. Aviral Kumar Tiwari, 2015. "Oil Price and Exchange Rate in Malaysia: A Time-Frequency Analysis," Asian Economic and Financial Review, Asian Economic and Social Society, vol. 5(4), pages 661-670, April.

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