A comparative time series analysis of crude mortality rate in the BRICS countries

Abstract

Proper research and analysis of mortality dynamics is essential to provide reliable economic information about any country. This paper deals with the historical comparative time series analysis of the mortality rate dynamics in the BRICS countries to determine their economic performances over the years. This article presents stochastic models based on autoregressive integrated moving average (ARIMA (p, d, q)) models of various orders with a view to identifying the optimal and comparative model for the crude death rate (CDR) in the BRICS countries. The ARIMA (p, d, q) models were formulated for the crude death rates in the BRICS countries and the overall annual crude death rate for the period 1960–2018. The optimal choice of ARIMA models of order p and q was selected for each of the series. The results indicate that the ARIMA (2, 2, 0) model was the optimal model for predicting mortality dynamics in the overall BRICS data. In addition, there was a significant decrease in trends (p-value < 2.22e-16) during the study period from 1960 to 2018. In addition, the crude death rate’s data for the BRICS countries proved to be mostly non-linear, non-seasonal and without structural breaks. Finally, the findings of this study were discussed and recognized as having relevant policy implications for forecasting, insurance planning, as well as for disaster or risk reduction in the context of unprecedented global happenings in the post-pandemic era.