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There has been significant use of stochastic mortality models that aim at estimating and forecasting mortality rates based on past trends. The vast use of these extrapolative methods has seen extension of various stochastic mortality models to include specific latent factors that accurately capture the cause of mortality. Two factor mortality models: Age-Period/ Age-Cohort have been most successful. The Lee- Carter Model has particularly been promising in terms of its accuracy in estimation and forecasting of future mortality trends. What remains unclear in estimation is the specific factors to include in the models so that it captures the death effects better. This paper proposes a pro-cyclical mortality model that includes macroeconomic factors i.e. GDP Per Capita growth rates and unemployment rates as additional parameters to the original Lee Carter Model and other extrapolative models. Three common extrapolative mortality models are chosen and model extensions are formulated and are run using the R software. Consequently, the fit to historical mortality rates is assessed for the proposed models whilst comparing the additional predictive power that they have over the original models. As a result, the most robust proposed mortality model is chosen to perform mortality projections and come up with forecasted mortality rates. The study is significant to actuaries, demographers and population studies experts who are interested in developing better mortality projections. In addition, it will aid actuaries in coming up with more reliable prices for financial products that are contingent on survival or death.