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Periodically correlated modeling by means of the periodograms asymptotic distributions

Author

Listed:
  • A. R. Nematollahi

    (Shiraz University)

  • A. R. Soltani

    (Shiraz University
    Kuwait University)

  • M. R. Mahmoudi

    (Shiraz University)

Abstract

In this paper, we introduce a test statistics to test whether a discrete time periodically correlated model with a given spectral density explains an observed time series. Our testing procedure is based on an application of the asymptotic distribution of the periodogram established in Soltani and Azimmohseni (Stat Plan Inference 137:1236–1242, 2007). We make comparisons between our procedure and the methods that are proposed by Broszkiewicz-Suwaj et al. (Physica A 336:196–205, 2004). It is observed that our testing procedure is more powerful. We illustrate the performance of the proposed methods in real and simulated data sets.

Suggested Citation

  • A. R. Nematollahi & A. R. Soltani & M. R. Mahmoudi, 2017. "Periodically correlated modeling by means of the periodograms asymptotic distributions," Statistical Papers, Springer, vol. 58(4), pages 1267-1278, December.
    • Handle: RePEc:spr:stpapr:v:58:y:2017:i:4:d:10.1007_s00362-016-0748-9
    DOI: 10.1007/s00362-016-0748-9
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    References listed on IDEAS

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    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. John D. Storey, 2002. "A direct approach to false discovery rates," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(3), pages 479-498, August.
    3. Harry L. Hurd & Neil L. Gerr, 1991. "Graphical Methods For Determining The Presence Of Periodic Correlation," Journal of Time Series Analysis, Wiley Blackwell, vol. 12(4), pages 337-350, July.
    4. Andrés Alonso & Daniel Peña & Juan Romo, 2006. "Introducing model uncertainty by moving blocks bootstrap," Statistical Papers, Springer, vol. 47(2), pages 167-179, March.
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    Cited by:

    1. T. Manouchehri & A. R. Nematollahi, 2019. "Periodic autoregressive models with closed skew-normal innovations," Computational Statistics, Springer, vol. 34(3), pages 1183-1213, September.
    2. Mahmoudi, Mohammad Reza & Baleanu, Dumitru & Mansor, Zulkefli & Tuan, Bui Anh & Pho, Kim-Hung, 2020. "Fuzzy clustering method to compare the spread rate of Covid-19 in the high risks countries," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    3. Mahmoudi, Mohammad Reza & Heydari, Mohammad Hossein & Roohi, Reza, 2019. "A new method to compare the spectral densities of two independent periodically correlated time series," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 160(C), pages 103-110.
    4. Mohammad Reza Mahmoudi & Abdol Rassoul Zarei, 2022. "Using Periodic Copula to Assess the Relationship Between Two Meteorological Cyclostationary Time Series Datasets," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(11), pages 4363-4388, September.

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