Coherent forecasts of mortality with compositional data analysis

• Published in: Demographic research Vol. 37; pp. 527 - 566
• Main Authors: Bergeron-Boucher, Marie-Pier, Canudas-Romo, Vladimir, Oeppen, Jim, Vaupel, James W.
• Format: Journal Article
• Language: English
• Published: Max Planck Institute for Demographic Research 01.01.2018
• Subjects: Age; Analysis; Analytical forecasting; coherent mortality forecasting models; compositional data; Data analysis; Death; Forecasting; Forecasting models; Forecasts and trends; Lee-Carter model; Li-Lee; Life expectancy; Life tables; Methods; Modeling; Mortality; mortality forecasts; Research Article; Time series forecasting
• ISSN: 1435-9871; 1435-9871
• DOI: 10.4054/DemRes.2017.37.17

Mortality trends for subpopulations, e.g., countries in a region or provinces in a country, tend to change similarly over time. However, when forecasting subpopulations independently, the forecast mortality trends often diverge. These divergent trends emerge from an inability of different forecast models to offer population-specific forecasts that are consistent with one another. Nondivergent forecasts between similar populations are often referred to as "coherent." We propose a new forecasting method that addresses the coherence problem for subpopulations, based on Compositional Data Analysis (CoDa) of the life table distribution of deaths. We adapt existing coherent and noncoherent forecasting models to CoDa and compare their results. We apply our coherent method to the female mortality of 15 Western European countries and show that our proposed strategy would have improved the forecast accuracy for many of the selected countries. The results also show that the CoDa adaptation of commonly used models allows the rates of mortality improvements (RMIs) to change over time.