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A Wavelet-Based Approach to Filter Out Symmetric Macroeconomic Shocks

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  • Roman Marsalek
  • Jitka Pomenkova
  • Svatopluk Kapounek

Abstract

We propose a novel method for econometric time series analysis. This method acts as the comovement-selective filter and is useful to filter out the cycles that are caused by an global event present in the reference time series. We demonstrate its applicability on removing symmetric macroeconomic shock caused by recent financial crisis from the business cycle of the euro area according to the comovement with the United States. The application allowing to identify the country specific business cycles in the Visegrad countries data using the comovement with Germany is also presented. The method is based on the continuous wavelet transform, its inverse and the comovement measurement in the time-frequency domain. Its application also enables to uncover detailed development of the business cycle synchronization in time. Copyright Springer Science+Business Media New York 2014

Suggested Citation

  • Roman Marsalek & Jitka Pomenkova & Svatopluk Kapounek, 2014. "A Wavelet-Based Approach to Filter Out Symmetric Macroeconomic Shocks," Computational Economics, Springer;Society for Computational Economics, vol. 44(4), pages 477-488, December.
    • Handle: RePEc:kap:compec:v:44:y:2014:i:4:p:477-488
    DOI: 10.1007/s10614-013-9403-x
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    1. de Carvalho, Miguel & Rodrigues, Paulo C. & Rua, António, 2012. "Tracking the US business cycle with a singular spectrum analysis," Economics Letters, Elsevier, vol. 114(1), pages 32-35.
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    5. Yogo, Motohiro, 2008. "Measuring business cycles: A wavelet analysis of economic time series," Economics Letters, Elsevier, vol. 100(2), pages 208-212, August.
    6. Alessandra Iacobucci & Alain Noullez, 2005. "A Frequency Selective Filter for Short-Length Time Series," Computational Economics, Springer;Society for Computational Economics, vol. 25(1), pages 75-102, February.
    7. Fidrmuc, Jarko & Ikeda, Taro & Iwatsubo, Kentaro, 2012. "International transmission of business cycles: Evidence from dynamic correlations," Economics Letters, Elsevier, vol. 114(3), pages 252-255.
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    Cited by:

    1. repec:prg:jnlpep:v:preprint:id:512:p:1-18 is not listed on IDEAS
    2. Kapounek, Svatopluk & Kučerová, Zuzana, 2019. "Historical decoupling in the EU: Evidence from time-frequency analysis," International Review of Economics & Finance, Elsevier, vol. 60(C), pages 265-280.
    3. Yi-Ting Chen & Wan-Ni Lai & Edward W. Sun, 2019. "Jump Detection and Noise Separation by a Singular Wavelet Method for Predictive Analytics of High-Frequency Data," Computational Economics, Springer;Society for Computational Economics, vol. 54(2), pages 809-844, August.
    4. Zuzana Kucerova & Jitka Pomenkova, 2014. "Financial and Trade Integration of Selected EU Regions: Dynamic Correlation and Wavelet Approach," MENDELU Working Papers in Business and Economics 2014-45, Mendel University in Brno, Faculty of Business and Economics.
    5. repec:prg:jnlpep:v:2015:y:2015:i:5:id:512:p:1-18 is not listed on IDEAS
    6. Jitka Poměnková & Roman Maršálek, 2015. "Empirical Evidence of Ideal Filter Approximation: Peripheral and Selected EU Countries Application," Prague Economic Papers, Prague University of Economics and Business, vol. 2015(5), pages 485-502.

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