Stochastic models of Nigerian total livebirths

This study aims at modelling Nigerian total livebirths data, and to select the appropriate model for the disaggregated livebirths series among the proposed univariate stochastic time series models, based on in-sample fitting. Forecast of demographic variables such as births has a great influence on the growth of a population with respect to its demands on various systems such as education, health, economy, and provision of social amenities for future population over a period of time. The result of the Dickey-Fuller test confirmed the stationarity of the livebirths series, after subjecting it to non-seasonal differencing and Box-Cox variance stabilizing transformation. Correlogram visual analysis method was employed to identify reasonable models for the total livebirths series; and the identified univariate models are ARIMA(1,1,0), and SARIMA(1,1,0)(0,0,1)4. The diagnostic checks to evaluate the adequacy of the fits of the models revealed that the residuals of the two univariate models were indeed serially uncorrelated. The results of other measures of adequacy verification and forecast accuracy suggest that the two univariate models provided adequate predictive models for the Nigerian total livebirths series, with the SARIMA model outperforming the ARIMA model. Thus the SARIMA model was chosen as the more appropriate model in fitting the livebirths data because of its overall fit as confirmed by its measures of forecast performance.