Random cohort effects and age groups dependency structure for mortality modelling and forecasting: Mixed-effects time-series model approach

TL;DR

This paper introduces a novel mixed-effects time-series model for mortality forecasting that accounts for age group dependencies and random cohort effects, improving forecast accuracy over existing models.

Contribution

It presents a new approach that captures cohort effects without pre-specified constraints, enhancing mortality modeling and forecasting accuracy.

Findings

Improved short-, mid-, and long-term forecast accuracy.

Effectively models cohort effects with uncertainty quantification.

Demonstrated superior performance over CBD model.

Abstract

There have been significant efforts devoted to solving the longevity risk given that a continuous growth in population ageing has become a severe issue for many developed countries over the past few decades. The Cairns-Blake-Dowd (CBD) model, which incorporates cohort effects parameters in its parsimonious design, is one of the most well-known approaches for mortality modelling at higher ages and longevity risk. This article proposes a novel mixed-effects time-series approach for mortality modelling and forecasting with considerations of age groups dependence and random cohort effects parameters. The proposed model can disclose more mortality data information and provide a natural quantification of the model parameters uncertainties with no pre-specified constraint required for estimating the cohort effects parameters. The abilities of the proposed approach are demonstrated through two applications with empirical male and female mortality data. The proposed approach shows remarkable improvements in terms of forecast accuracy compared to the CBD model in the short-, mid-and long-term forecasting using mortality data of several developed countries in the numerical examples.