Khan Naushad Mamode, Soobhug Ashwinee Devi, Youssef Noha, Fedally Swalay, Nadarajah Saralees, Heetun Zaid
Faculty of Social Sciences and Humanities, University of Mauritius, Réduit, Mauritius.
Statistics Mauritius, Ministry of Finance, Economic Planning and Development, Port-Louis, Mauritius.
Healthc Anal (N Y). 2022 Nov;2:100086. doi: 10.1016/j.health.2022.100086. Epub 2022 Aug 9.
The COVID-19 series is obviously one of the most volatile time series with lots of spikes and oscillations. The conventional integer-valued auto-regressive time series models (INAR) may be limited to account for such features in COVID-19 series such as severe over-dispersion, excess of zeros, periodicity, harmonic shapes and oscillations. This paper proposes alternative formulations of the classical INAR process by considering the class of high-ordered INAR models with harmonic innovation distributions. Interestingly, the paper further explores the bivariate extension of these high-ordered INARs. South Africa and Mauritius' COVID-19 series are re-scrutinized under the optic of these new INAR processes. Some simulation experiments are also executed to validate the new models and their estimation procedures.
新冠疫情系列显然是最具波动性的时间序列之一，有许多峰值和振荡。传统的整数自回归时间序列模型（INAR）可能难以解释新冠疫情系列中的此类特征，如严重的过度离散、零值过多、周期性、谐波形状和振荡。本文通过考虑具有谐波创新分布的高阶INAR模型类别，提出了经典INAR过程的替代公式。有趣的是，本文进一步探索了这些高阶INAR的二元扩展。在这些新的INAR过程的视角下，对南非和毛里求斯的新冠疫情系列进行了重新审视。还进行了一些模拟实验，以验证新模型及其估计程序。
