Abstract:

Data on Birth registration often comes from complex sampling designs with hierarchical structure. The study aimed at using a novel approach and expanded variables on the determinants of birth registration. Issues of live birth registration among young children in developing countries are gaining more attention of policy-makers because of the adverse effects on the wellbeing of people and economic of these nations. Civil registration provides a continuous record of vital events. This research examines the stationarity in live birth registration among the two age-groups and a model was fitted to the age-groups. The data used were analyzed using R programming language. Time series analysis was used to analyze and forecast from the data presented. Research results showed that group under 1 year and 5 years are stationary, only under 5 years forecast indicated that there is a constant in the data and Box- Ljung showed that the residual plots are dependent. It was concluded that there is substantial growth in the registration of live birth in Nigeria despite the instability in the system during the specified period. Also, the Augmented Dickey-Fuller (ADF) test and Philip Perron (PP) test imply that the more negative the test, the stronger is the model. Seasonal autoregressive integrated moving average (SARIMA) approach model stands as the best model for prediction based on this series.