Adjustment and Coverage of Variance Projection Error: Application of Bayesian Inference in Mortality Projections

Models developed to project mortality are primarily based on extrapolative methods and to some extent researchers’ subjective judgement. These models face the same challenge of detecting the inherent uncertainty in forecasting. This paper introduces Bayesian inference methods for mortality projections in response to such a problem and its methodological importance. As a developed country with an accurate registration system, the French mortality data was used to estimate and predict mortality rates from 1959 to 1999. Bayesian inference was used to estimate each parameter's posterior and prior distributions, and the Monte Carlo Markov chain algorithm was exploited to estimate the parameters. The findings of the research indicate that in Bayesian models, by examining the entire space of a parameter through probability distributions, a better estimate of a parameter values is obtained. A Bayesian model also has a wider confidence interval than the Lee-Carter model, covering a more significant part of errors and uncertainty in most age groups.