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), 2024. Published by Science Publishing Group |
Self-Exciting Threshold Autoregressive (SETAR) Model, Structural Breaks, COVID-19, Nonstationary Series, Nonlinear Series
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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
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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Download@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}
}
TY - JOUR T1 - Self-Exciting Threshold Autoregressive Modelling of COVID-19 Confirmed Daily Cases in Nigeria AU - Nwakuya Maureen Tobechukwu AU - Biu Oyinebifun Emmanuel AU - Benson Tina Ibienebaka Y1 - 2022/11/23 PY - 2022 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 SP - 182 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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