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Methods for determining the presence of periodic correlation based on the bootstrap methodology

Author

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  • Ewa Broszkiewicz-Suwaj

Abstract

This paper presents methods for detecting the period of non Gaussian PC processes. A new statistic for testing periodic correlation is proposed. It is based on the bootstrap procedure which is used to estimate confidence intervals of coherence statistic. This method is linked to that of Hurd and Gerr based on Goodman's tests so both methodologies are also compared. It is demonstrated that in some situations the new test appears to be better.

Suggested Citation

  • Ewa Broszkiewicz-Suwaj, 2003. "Methods for determining the presence of periodic correlation based on the bootstrap methodology," HSC Research Reports HSC/03/02, Hugo Steinhaus Center, Wroclaw University of Science and Technology.
    • Handle: RePEc:wuu:wpaper:hsc0302
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    File URL: http://www.im.pwr.wroc.pl/~hugo/RePEc/wuu/wpaper/HSC_03_02.pdf
    File Function: Original version, 2003
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    Citations

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    Cited by:

    1. Broszkiewicz-Suwaj, E & Makagon, A & Weron, R & Wyłomańska, A, 2004. "On detecting and modeling periodic correlation in financial data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 336(1), pages 196-205.
    2. Mohammad Reza Mahmoudi & Mohsen Maleki, 2017. "A new method to detect periodically correlated structure," Computational Statistics, Springer, vol. 32(4), pages 1569-1581, December.
    3. Mitra Ghanbarzadeh & Mina Aminghafari, 2016. "A Wavelet Characterization of Continuous-Time Periodically Correlated Processes with Application to Simulation," Journal of Time Series Analysis, Wiley Blackwell, vol. 37(6), pages 741-762, November.

    More about this item

    Keywords

    Periodic correlation; Bootstrap; Spectral representation;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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