On Bivariate Copula Modelling: An application to Infant Mortality and Fertility Rate Data
Gabriel Asare Okyere*, Godfred Zaachi, Emmanuel K Owusu Mintah, Charles K Amponsah and Daniel A Alhassan
Department of Statistic and Actuarial Science, Kwame Nkrumah University of Science and Technology, PMB, Kumasi, Ghana
*Corresponding Author: Gabriel Asare Okyere, Department of Statistic and Actuarial Science, Kwame Nkrumah University of Science and Technology, PMB, Kumasi, Ghana.
June 21,2021; Published: October 27, 2021
Infant survival is key to the new era of Sustainable Development Goals (SDGs). However, in Africa and Ghana especially, this is challenged by the alarming Infant Mortality Rate (IMR) - a direct consequence of population growth characterized by high fertility. The purpose of our study is to investigate the extent to which Infant Mortality Rate(IMR) occur conditionally on Total Fertility Rate(TFR) over the years and how these indicators are related using data from 1960 to 2020. We highlight the application of copula models in dealing with interdependencies between IMR and the TFR. In this study we compare several copula models using the differences in Akaike Information Criterion (AIC) to select the most appropriate" model for our data. The results indicate that the bivariate Clayton copula with continuous. Weibull margins best" describe the conditional distribution of IMR. Our results further indicate a 90.40% chance that IMR will exceed 120.5 deaths per 1000 live births if the TFR rise to 7 children per woman. We also conclude base on our model estimate that 2021 infant mortality will exceed its 2020 estimated value of 32.80 deaths per 1000 live births given the current fertility rate of 4 births per woman with a chance as low as 2%.
Keywords: Aikaike Information Criteria (AIC); Copula; Infants Mortality Rate; Marginal Distribution; Total Fertility Rate
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