An iterative least-squares Monte Carlo approach for the simulation of cohort based biometric indices

This paper tackles the problem of approximating the distribution of future biometric indices under a cohort-based perspective. Unlike period-based evaluations, cohort-based schemes require the computation of conditional expectations for which explicit solutions often do not exist. To overcome this issue, we suggest the application of a well-established methodology, i.e., the Least-Squares Monte Carlo approach. The idea is to approximate conditional expectations by combining simulations and regression techniques, thus avoiding a straightforward but computationally demanding nested simulations method. To show the extreme flexibility and generality of the proposal, we provide extensive numerical results concerning two main longevity indices, life expectancy and lifespan disparity, obtained by adopting both single- and multi-population mortality models. Comparisons between period- and cohort-based results are made as well. Finally, the paper shows that the proposed methodology can be used to approximate other biometric indices at future dates for which cohort-based estimations are often replaced by period ones for computational simplicity.