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Time series analysis of the number of Covid-19 deaths in Iraq | ||
| International Journal of Nonlinear Analysis and Applications | ||
| دوره 12، شماره 2، بهمن 2021، صفحه 1997-2007 اصل مقاله (734.77 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2021.5331 | ||
| نویسندگان | ||
| Sarab D. Shukur* ؛ Tasnim Hasan Kadhim | ||
| Department of Mathematics, College of Science, University of Baghdad, Iraq | ||
| تاریخ دریافت: 10 فروردین 1400، تاریخ بازنگری: 24 خرداد 1400، تاریخ پذیرش: 14 تیر 1400 | ||
| چکیده | ||
| In this paper, the time series data for the number of deaths from Coronavirus (COVID-19) patients in Iraq were analyzed for the period from 4/3/2020 to 18/2/2021. ARCH, GARCH, and TGARCH models were applied due to the changing volatility of the series leading to a heteroscedastic variance. The appropriate models for the series were diagnosed and the best model was chosen and used for forecasting by the exponential smoothing methods. The comparison criterion used was the Root Mean Squared Error and the Sum of Squared Residuals. The most appropriate model for modeling and forecasting the Coronavirus deaths series in Iraq was diagnosed as TGARCH (1,1). Finally, the method of Holt-winter-additive forecasting was the best method among the | ||
| کلیدواژه‌ها | ||
| COVID-19؛ Time Series؛ volatility؛ heteroscedastic variance؛ GARCH؛ ARCH؛ TGARCH | ||
| مراجع | ||
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[1] G. Ali, EGARCH, GJR-GARCH, TGARCH, AVGARCH, NGARCH, IGARCH and APARCH models for pathogens at marine recreational sites, J. Stat. Econ. Meth. 2 (2013) 57–73. [2] P. Catani and N. Ahlgren, Combined Lagrange multiplier test for ARCH in vector autoregressive models, Econ. Stat. 1 (2017) 62–84. [3] Z. Ceylan, Estimation of COVID-19 prevalence in Italy, Spain, and France, Sci. Total Environ. 729 (2020) 1–8. [4] P. Cheteni, Stock market volatility using GARCH models: Evidence from South Africa and China stock markets, MPRA, 77355 (2016) 1–13. [5] J.H. Cochrane, Time series for macroeconomics and finance, Manuscript, University of Chicago, Chicago, 2005. [6] R. Engle, Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation, J. Econ. 50 (1982) 987–1007. [7] C. Francq and J.M. Zakoian, GARCH Models: Structure, Statistical Inference and Financial Applications, John Wiley & Sons, 2019. [8] A. Grek, Forecasting accuracy for ARCH models and GARCH (1, 1) family: Which model does best capture the volatility of the Swedish stock markets, Orebro University, Advanced Level Thesis, 2014. ¨ [9] T. C. Mills, Applied Time Series Analysis: A Practical Guide to Modeling and Forecasting, Academic Press, London, 2019. [10] V.P. Papastefanopoulos, Linardatos and S. Kotsiantis, COVID-19: a comparison of time series methods to forecast percentage of active cases per population, Appl. Sci. 10 (2020) 38–80. [11] E. Rossi, Lecture Notes on GARCH Models, University of Pavia, 2004. [12] R.H. Shumway, D. S. Stoffer and D.S. Stoffer, Time Series Analysis and its Applications, Springer, London, 2015. [13] W.W. Wei, Time Series Analysis, Oxford Handbook of Quantitative Methods in Psychology, 2013. | ||
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