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Published in:
2020 | OriginalPaper | Chapter
A Comparison of Generalized Stochastic Milevsky-Promislov Mortality Models with Continuous Non-Gaussian Filters
Authors : Piotr Śliwka, Leslaw Socha
Published in: Computational Science – ICCS 2020
Publisher: Springer International Publishing
Abstract
The ability to precisely model mortality rates \(\mu _{x,t}\) plays an important role from the economic point of view in healthcare. The aim of this article is to propose a comparison of the estimation of the mortality rates based on a class of stochastic Milevsky-Promislov mortality models. We assume that excitations are modeled by second, fourth and sixth order polynomials of outputs from a linear non-Gaussian filter. To estimate the model parameters we use the first and second moments of \(\mu _{x,t}\). The theoretical values obtained in both cases were compared with theoretical \(\widehat{\mu _{x,t}}\) based on a classical Lee-Carter model. The obtained results confirm the usefulness of the switched model based on the continuous non-Gaussian processes used for modeling \(\mu _{x,t}\).