Visualizing and forecasting subnational life-table death counts: Gap forecasting methods

2026-07-08

AI Summary

This study addresses the modeling and forecasting of sex- and region-specific death counts in life tables by proposing a novel joint framework that simultaneously captures gender gaps, regional disparities, and their interaction. Built upon national-level data and prioritizing female mortality data, the approach employs a cumulative distribution function transformation to accommodate the non-negativity and additivity constraints inherent in death counts, enabling accurate multi-step-ahead forecasts from 1 to 15 steps. Empirical analysis using Japanese data from 1947 to 2023 demonstrates that the proposed model significantly outperforms benchmark methods in both point and interval forecast accuracy, while also effectively uncovering key drivers underlying sex- and region-specific mortality differentials.

Abstract

Subnational life-table death counts are highly correlated across time and space and differ by gender. While these associations are helpful in improving forecasts through joint modeling, less attention has been paid to identifying and understanding mortality disparities in gender and regional gaps. We propose a forecasting framework to model and forecast female life-table death counts at the national or subnational level, and to model and forecast the associated gender gap. For either females or males, we could forecast national life-table death counts and the regional gap relative to national data. By combining gender and regional gaps, we also explore the double gap by prioritizing national data and female data. Life-table death counts are unique due to their non-negativity and summability constraints. To address the constraints, we apply a one-to-one transformation, termed cumulative distribution function transformation, to obtain one- to 15-step-ahead forecasts. Using Japanese age-specific life-table death counts between ages 0 and 110+ from 1947 to 2023, we evaluate and compare point and interval forecast accuracy across gender, region, and double gaps. By focusing on these gaps, we can deepen our understanding of the possible factors driving gender and regional mortality variations.

Problem

• mortality disparities
• gender gap
• regional gap
• life-table death counts
• subnational forecasting

Innovation

• gap forecasting
• life-table death counts
• cumulative distribution function transformation
• gender gap
• regional disparity