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Self-Exciting Threshold Autoregressive Modelling of COVID-19 Confirmed Daily Cases in Nigeria

Received: 1 November 2022     Accepted: 15 November 2022     Published: 23 November 2022
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Abstract

This article proposed the modelling of the daily COVID-19 confirmed cases in Nigeria using a Self-Exciting Threshold Autoregressive (SETAR) model. Coronavirus also known as Covid-19 first appeared in Wuhan in December 2019 and quickly spread across the world and became a major phenomenon confronting humanity today. Since the outbreak of COVID-19, several models have been introduced to study the virus and recommend appropriate policy direction to tackle the pandemic. Due to the nonlinear behavior of the series, the Self-Exciting Threshold Autoregressive (SETAR) model was adopted. The series was found to be nonstationary series which was differenced twice to achieve stationarity. The series exhibited nonlinearity with evidence of a structural break. A SETAR (2, 4, 1) model was identified as the most fitted model to the data. Furthermore, the identified SETAR nonlinear model was used to obtain a one month period forecasts for the daily confirmed COVID-19 cases. The forecast accuracy measure were used to verify that SETAR (2, 4, 1) was the best fitted model and forecast for the month of January 2023 was presented. The result also evidenced that the number of daily confirmed cases is expected to increase from 281,526 cases in year 2022 to 312,776 cases in year 2023.

Published in International Journal of Data Science and Analysis (Volume 8, Issue 6)
DOI 10.11648/j.ijdsa.20220806.12
Page(s) 182-186
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2022. Published by Science Publishing Group

Keywords

Self-Exciting Threshold Autoregressive (SETAR) Model, Structural Breaks, COVID-19, Nonstationary Series, Nonlinear Series

References
[1] Aydın D., Güneri and Öznur İşçi (2015). Time Series Prediction Using Hybridization of AR, SETAR and ARM Models. International Journal of Applied Science and Technology, 5 (6).
[2] Ayinde, K., Lukman, A. F., Rauf, R. I., Alabi, O. O., Okon, C. E. & Ayinde, O. E. (2020). Modeling Nigerian Covid-19 cases: A comparative analysis of models and estimators. Chaos, Solitons & Fractals, 138 (2020), 109-119.
[3] Benson T. I., Biu E. O. and Nwakuya M. T. (2022). Markov Switching Autoregressive Modelling of the COVID-19 Daily Cases and Deaths in Nigeria. American Journal of Mathematics and Statistics, 12 (2); 15-21. DOI: 10.5923/j.ajms.20221202.01
[4] Clements, M. P. and Smith, J. (2001). The performance of alternative forecasting methods for SETAR models, International Journal of Forecasting 13; 463-75.
[5] de Goojier, J. G. (2001). On threshold moving-average models. Journal of Time Series Analysis, 19; 1–18. MR1624163.
[6] Feng, H., Liu, J. (2002). A SETAR Model for Canadian GDP: Non-linearities and Forecast Comparisons. University of Victoria, Working Paper EWP 0206, ISSN 1485-6441.
[7] Nwakuya M. T. and Biu E. O (2022). ARFIMA Modelling And Long Memory: The Case Of COVID19 Daily Deaths In Nigeria. International Journal of Innovative Mathematics, Statistics & Energy Policies 10 (4); 47-59.
[8] Said, S. E. and Dickey, D. A. (1984). Testing for Unit Roots in Autoregressive-Moving Average Models of Unknown Order. Biometrika, 71; 599-607.
[9] Tong, H. (1978). On a threshold model, Pattern Recognition and Signal Processing, Amsterdam: Sijthoff & Noordhoff, 101-41.
[10] Tong, H. (1983). Threshold models in non-linear time series analysis, Lecture Notes in Statistics, No. 21, Heidelberg: Springer, 62-64.
[11] Tong, H. (1990). Non-linear Time Series: A Dynamical System Approach. Oxford: Oxford University Press.
[12] Tong, H. and Lim, K. S. (1980). Threshold autoregression, limit cycles and cyclical data (with discussion), Journal of the Royal Statistical Society, Series B, Vol. 42 (3), 245-292.
[13] Tong, H. & Yeung, I. (1991). On tests for Self-exciting Threshold Autoregressive-Type Non-linearity in Partially Observed Time Series. Applied Statistics, 40; 43-62.
[14] Tsay, R. S. (1989). Testing and modeling threshold autoregressive processes. Journal of the Royal Statistical Society B, 84; 231-240.
[15] Watier, L. and Richardson, S. (1995). Modelling of an epidemiological time series by a threshold autoregressive model. The Statistician 44 (3); 353-364.
[16] WHO (2020). WHO Director-General's remarks at the media briefing on 2019-nCoV on 11 February 2020. WHO. https://www.who.int/director-general/speeches/detail/who-director-general-s-remarks-at-the-media-briefing-on-2019-ncov-on-11-february-2020.
Cite This Article
  • APA Style

    Nwakuya Maureen Tobechukwu, Biu Oyinebifun Emmanuel, Benson Tina Ibienebaka. (2022). Self-Exciting Threshold Autoregressive Modelling of COVID-19 Confirmed Daily Cases in Nigeria. International Journal of Data Science and Analysis, 8(6), 182-186. https://doi.org/10.11648/j.ijdsa.20220806.12

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    ACS Style

    Nwakuya Maureen Tobechukwu; Biu Oyinebifun Emmanuel; Benson Tina Ibienebaka. Self-Exciting Threshold Autoregressive Modelling of COVID-19 Confirmed Daily Cases in Nigeria. Int. J. Data Sci. Anal. 2022, 8(6), 182-186. doi: 10.11648/j.ijdsa.20220806.12

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    AMA Style

    Nwakuya Maureen Tobechukwu, Biu Oyinebifun Emmanuel, Benson Tina Ibienebaka. Self-Exciting Threshold Autoregressive Modelling of COVID-19 Confirmed Daily Cases in Nigeria. Int J Data Sci Anal. 2022;8(6):182-186. doi: 10.11648/j.ijdsa.20220806.12

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  • @article{10.11648/j.ijdsa.20220806.12,
      author = {Nwakuya Maureen Tobechukwu and Biu Oyinebifun Emmanuel and Benson Tina Ibienebaka},
      title = {Self-Exciting Threshold Autoregressive Modelling of COVID-19 Confirmed Daily Cases in Nigeria},
      journal = {International Journal of Data Science and Analysis},
      volume = {8},
      number = {6},
      pages = {182-186},
      doi = {10.11648/j.ijdsa.20220806.12},
      url = {https://doi.org/10.11648/j.ijdsa.20220806.12},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20220806.12},
      abstract = {This article proposed the modelling of the daily COVID-19 confirmed cases in Nigeria using a Self-Exciting Threshold Autoregressive (SETAR) model. Coronavirus also known as Covid-19 first appeared in Wuhan in December 2019 and quickly spread across the world and became a major phenomenon confronting humanity today. Since the outbreak of COVID-19, several models have been introduced to study the virus and recommend appropriate policy direction to tackle the pandemic. Due to the nonlinear behavior of the series, the Self-Exciting Threshold Autoregressive (SETAR) model was adopted. The series was found to be nonstationary series which was differenced twice to achieve stationarity. The series exhibited nonlinearity with evidence of a structural break. A SETAR (2, 4, 1) model was identified as the most fitted model to the data. Furthermore, the identified SETAR nonlinear model was used to obtain a one month period forecasts for the daily confirmed COVID-19 cases. The forecast accuracy measure were used to verify that SETAR (2, 4, 1) was the best fitted model and forecast for the month of January 2023 was presented. The result also evidenced that the number of daily confirmed cases is expected to increase from 281,526 cases in year 2022 to 312,776 cases in year 2023.},
     year = {2022}
    }
    

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    T1  - Self-Exciting Threshold Autoregressive Modelling of COVID-19 Confirmed Daily Cases in Nigeria
    AU  - Nwakuya Maureen Tobechukwu
    AU  - Biu Oyinebifun Emmanuel
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    N1  - https://doi.org/10.11648/j.ijdsa.20220806.12
    DO  - 10.11648/j.ijdsa.20220806.12
    T2  - International Journal of Data Science and Analysis
    JF  - International Journal of Data Science and Analysis
    JO  - International Journal of Data Science and Analysis
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    EP  - 186
    PB  - Science Publishing Group
    SN  - 2575-1891
    UR  - https://doi.org/10.11648/j.ijdsa.20220806.12
    AB  - This article proposed the modelling of the daily COVID-19 confirmed cases in Nigeria using a Self-Exciting Threshold Autoregressive (SETAR) model. Coronavirus also known as Covid-19 first appeared in Wuhan in December 2019 and quickly spread across the world and became a major phenomenon confronting humanity today. Since the outbreak of COVID-19, several models have been introduced to study the virus and recommend appropriate policy direction to tackle the pandemic. Due to the nonlinear behavior of the series, the Self-Exciting Threshold Autoregressive (SETAR) model was adopted. The series was found to be nonstationary series which was differenced twice to achieve stationarity. The series exhibited nonlinearity with evidence of a structural break. A SETAR (2, 4, 1) model was identified as the most fitted model to the data. Furthermore, the identified SETAR nonlinear model was used to obtain a one month period forecasts for the daily confirmed COVID-19 cases. The forecast accuracy measure were used to verify that SETAR (2, 4, 1) was the best fitted model and forecast for the month of January 2023 was presented. The result also evidenced that the number of daily confirmed cases is expected to increase from 281,526 cases in year 2022 to 312,776 cases in year 2023.
    VL  - 8
    IS  - 6
    ER  - 

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Author Information
  • Department of Mathematics and Statistics, University of Port Harcourt, Choba, Nigeria

  • Department of Mathematics and Statistics, University of Port Harcourt, Choba, Nigeria

  • Department of Mathematics Statistics, Ignatius Ajuru University of Education, Obukuru, Nigeria

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