The vector autoregressive model with asymmetric shocks for the new cases and the new deaths of Covid-19 in Iran

AbstractThe novel corona virus (covid-19) spread quickly from person to another and one of the basic aspects of the country management has been to prevent the spread of this disease. So the prediction of its expansion is very important. In such matters, the estimation of new cases and deaths in covid-19 has been considered by researchers. we propose an estimation of the statistical model for predicting the new cases and the new deaths by using the vector autoregressive (VAR) model with the multivariate skew normal (MSN) distribution for the asymmetric shocks and predict the samples data. The maximum likelihood (ML) method is applied to estimation of this model for the weekly data of the new cases and the new deaths of covid-19. Data are taken from World Health Organization (WHO) from March 2020 until March 2023 Iran country. The performance of the model is evaluated with the Akaike and the Bayesian information criterions and the mean absolute prediction error (MAPE) is interpreted.

کلیدواژه های The vector autoregressive model with asymmetric shocks for the new cases and the new deaths of Covid-19 in Iran:

covid19 ، Forecasting ، Maximum likelihood estimation ، Multivariate skew normal ، Skewness ، Vector autoregressive