Lee-Carter Mortality Forecasting: Application to Mauritian Population
Woodun Dhandevi1, Ho Ming Kang2, Raja Rajeswary Ponnusamy3
1Woodun Dhandevi, School of Mathematics, Actuaries and Quantitative Studies Asia Pacific University of Technology and Innovation, Malaysia.
2Ho Ming Kang, School of Mathematics, Actuaries and Quantitative Studies Asia Pacific University of Technology and Innovation, Malaysia.
3Raja Rajeswary Ponnusamy, School of Mathematics, Actuaries and Quantitative Studies Asia Pacific University of Technology and Innovation Malaysia.
Manuscript received on 05 February 2019 | Revised Manuscript received on 11 February 2019 | Manuscript Published on 19 February 2019 | PP: 169-175 | Volume-7 Issue-5S January 2019 | Retrieval Number: ES2143017519/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Recent decades have perceived remarkable improvements in life expectancies which have further driven significant declines in mortality. The unremitting decrease in mortality rates and its systematic underestimation has been drawing the substantial attention of researchers because of its impending effect on population size and structure, social security systems and from an actuarial perspective, the life insurance and pension industry worldwide. The Lee-Carter model has been widely accepted by actuaries among all the projections methods. This paper applies the Lee-Carter model to forecast the mortality rates of Mauritius for the next 20 years. The empirical mortality data sets of the Mauritian population for the period of 1984 to 2016 obtained from the Statistics Department of Mauritius was considered. The index of the level of mortality for each gender, the shape and the sensitivity coefficients for the ages 0 to 85 were obtained using the mortality forecasting model. The Singular Value Decomposition (SVD) was used to forecast the general mortality index for the period from 2017 to 2036. The software R Programme was used to generate the next two decades forecasted death rates of the Mauritian population. The future death rates were assessed using the measures of errors such as Mean Square Error (MSE), Akaike Information Criterion (AIC) and Bayesian Information Criterion (AIC). Appropriate life tables, also known as mortality tables are highly important for pricing and reserving in insurance and pension industries. The main objective of this research was to develop unabridged life tables for the Mauritian population using the projected death rates from the Lee-Carter model. With the aid of the forecasted mortality rates, mortality tables for male, female and total population were obtained from R software. Finally, the results indicate that the Lee-Carter model fitted the Mauritian mortality data reasonably well, with a percentage variation explained by Lee-Carter of 80.7%. Forecasted death rates from 2017 to 2036 had low values of MSE, AIC and BIC, showing high accuracy in the results. However, reliability tests were not conducted on the generated mortality tables, owing to time-constraint.
Keywords: Lee Carter Model, SVD, Life Expectancies.
Scope of the Article: Application Specific ICs (ASICs)