Forecasting age distribution of deaths across countries: Life expectancy and annuity valuation

TL;DR

This study evaluates and compares compositional mortality forecasting methods across 24 countries, focusing on transformations that improve visualization and accuracy for demographic and actuarial applications.

Contribution

It introduces a comprehensive validation of mortality modeling transformations, highlighting the effectiveness of the cumulative distribution function in cross-country mortality forecasting.

Findings

Cumulative distribution function enhances visualization of gender and country heterogeneity.

Forecast accuracy varies between transformations, with the CDF providing better scale-free insights.

Results support improved mortality and life expectancy predictions for demographers and actuaries.

Abstract

In this paper, we provide a comprehensive cross-country validation study of compositional mortality modeling and forecasting methods. Thus, we consider two one-to-one transformations: the cumulative distribution function and the centered log-ratio transformation in compositional data analysis. Between the two transformations, the cumulative distribution function provides a scale-free way to visualize the gender gap and cross-country heterogeneity in the probability of dying by sex and country. Drawing on age-specific period life-table death counts from 24 countries in the Human Mortality Database (2025), we assess and compare the point and interval forecast accuracy of the two transformations, using the same forecasting method. Enhancing the forecast accuracy of period life-table death counts is of significant value to demographers, who rely on such forecasts to estimate survival probabilities and life expectancy, and to actuaries, who use them to price annuities across various entry ages and maturities.