Abstract
Using annual time series data on neonatal deaths in Zimbabwe from 1966 to 2018, we model and forecast number of neonatal deaths over the next 25 years using the Box – Jenkins ARIMA technique. Diagnostic tests such as the ADF tests show that Neonatal Deaths (ND) series is I (2). Based on the AIC, the study presents the ARIMA (8, 2, 0) model as the optimal model. The diagnostic tests further indicate that the presented model is stable and its residuals are stationary in levels. The results of the study reveal that the numbers of neonatal deaths per year are expected to decline sharply in the next 25 years. In order to keep on reducing neonatal deaths in Zimbabwe, the study offered a four-fold policy prescription.
Highlights
Neonatal death can be defined as the number of neonates dying before reaching 28 days of age (Usman et al 2019)
In order to enhance the prevention of neonatal deaths, modeling and forecasting neonatal deaths is critical, especially in developing countries such as Zimbabwe where neonatal deaths are still prevalent in large numbers
Autoregressive Integrated Moving Average (ARIMA) models were developed by Box & Jenkins (1970) and their approach of identification, estimation and diagnostics is based on the principle of parsimony (Asteriou & Hall, 2007)
Summary
Neonatal death can be defined as the number of neonates dying before reaching 28 days of age (Usman et al 2019). The first 2 days after birth account for over 50% neonatal deaths, while the first week of life accounts for over 75% of all neonatal deaths (Carlo & Travers, 2016). 2.6 million children died in the first month of life in 2016 – nearly 7000 newborn deaths every day – most of which occurred in the first week, with about 1 million dying on the first day and close to 1 million dying within the 6 days (UNICEF, 2017). In order to enhance the prevention of neonatal deaths, modeling and forecasting neonatal deaths is critical, especially in developing countries such as Zimbabwe where neonatal deaths are still prevalent in large numbers. To forecast the number of neonatal deaths for the out-of sample period. To examine the pattern of neonatal deaths for the out-of-sample period
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