Abstract

Evaluating the predictive ability of mortality forecasts is important yet difficult. Death rates and mean lifespan are basic life table functions typically used to analyze to what extent the forecasts deviate from their realized values. Although these parameters are useful for specifying precisely how mortality has been forecasted, they cannot be used to assess whether the underlying mortality developments are plausible. We therefore propose that in addition to looking at average lifespan, we should examine whether the forecasted variability of the age at death is a plausible continuation of past trends. The validation of mortality forecasts for Italy, Japan, and Denmark demonstrates that their predictive performance can be evaluated more comprehensively by analyzing both the average lifespan and lifespan disparity—that is, by jointly analyzing the mean and the dispersion of mortality. Approaches that account for dynamic age shifts in survival improvements appear to perform better than others that enforce relatively invariant patterns. However, because forecasting approaches are designed to capture trends in average mortality, we argue that studying lifespan disparity may also help to improve the methodology and thus the predictive ability of mortality forecasts.

Highlights

  • The aim of most mortality forecasts is to predict how many additional years of life people will gain in the future

  • The objective of this article is to highlight the necessity to assess whether mortality forecasting methods can capture and forecast different trends of life expectancy at birth and lifespan disparity—that is, expose the benefits of incorporating lifespan disparity as an additional indicator in the toolkit that is used to evaluate the performance of mortality forecasts

  • Our analysis has shown that some methods—among them, the original Lee-Carter model, which is considered a golden standard in mortality forecasting—struggle to account for trends in lifespan disparity

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Summary

Introduction

The aim of most mortality forecasts is to predict how many additional years of life people will gain in the future. Goodness-of-fit tests as well as validation procedures are typically used to evaluate the predictive ability of mortality forecasts. Forecast errors can be expressed in absolute or relative terms, and they can be averaged over dimensions such as age, time, and population (Booth et al 2006; Keilman and Pham 2004; Koissi et al 2006; Shang et al 2011; Smith et al 2001). The meaning of these errors changes in each case. Shang (2015) recently proposed using test statistics to reveal significant differences in the forecast accuracy of point and interval estimates as well as differences between the forecasts of multiple approaches

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