Comparison of the Autoregressive Vector VAR with the Dynamic Error Correction Vector DVECM for Modeling COVID-19 Deaths

Abstract

In this article, the Vector Auto-Regressive model and the Dynamic Error Correction Vector Model will be used in modeling data representing the number of deaths due to infection with the COVD-19 virus as a dependent variable and the variable platelet rate in the blood as an independent variable and finding the model equations that represent the relationship between the two variables using the two models and then estimating the equations that were obtained by estimating the two models using the least squares method, then choosing the best estimated equation from each model, and then, using the standard error of the regression and the coefficient of determination, selecting the best equations from the two models. The Dynamic Error Correction Vector Model is superior to the Vector Auto-Regressive model in assessing the link between corona virus mortality and the proportion of platelets in the blood, according to the analysis carried out using the E-Views application, and that there is a direct relationship through the equation for the Dynamic Error Correction Vector Model between the deaths of the corona virus and the proportion of platelets in the blood in the long term, which is logical as a result of the increase in the impact of the deaths of the Corona virus, the increase in the platelet rate, and thus the increase in the deaths of the corona virus.