Alternative Forecasts of Danish Life Expectancy

Abstract

In the last three decades, considerable progress in mortality forecasting has been achieved, with new and more sophisticated models being introduced. Most of these forecasting models are based on the extrapolation of past trends, often assuming linear (or log-linear) development of mortality indicators, such as death rates or life expectancy. However, this assumption can be problematic in countries where mortality development has not been linear, such as in Denmark. Life expectancy in Denmark experienced stagnation from the 1980s until the mid-1990s. To avoid including the effect of the stagnation, Denmark’s official forecasts are based on data from 1990 only. This chapter is divided into three parts. First, we highlight and discuss some of the key methodological issues for mortality forecasting in Denmark. How many years of data are needed to forecast? Should linear extrapolation be used? Second, we compare the forecast performance of 11 models for Danish females and males and for period and cohort data. Finally, we assess the implications of the various forecasts for Danish society, and, in particular, their implications for future lifespan variability and age at retirement.

Demographic perspectives on the rise of longevity

“…Each model is based on specific assumptions about future mortality, e.g., constant rate of improvement, convergence toward a benchmark, etc. These models are also often sensitive to different factors or choices made by the forecasters, such as the length of the fitting period, the indicator used, or if a coherent model is used, to the choice of the reference populations ( 93 – 95 ).…”

Mortality forecasting at age 65 and above: an age-specific evaluation of the Lee-Carter model

“…Subjective decisions thus have to be made regarding the fitting period, which affects the forecast. For females, it seems that longer fitting periods provide more robust and accurate forecasts, as also shown by Bergeron-Boucher et al (2020).…”

A modal age at death approach to forecasting mortality

“…It is often caused by mortality worsening at young or middle-ages (Lindahl-Jacobsen et al 2016;Woolf and Schoomaker 2019). For example, in Denmark, life expectancy stagnates for females between 1975 and 1995, mainly due to limited mortality improvement at middle-ages and an important burden from cancer mortality from specific birth cohorts (Bergeron-Boucher et al 2019b;Christensen et al 2010). However, no such stagnation is observed at older ages and, as a result, the modal age at death has been increasing in Denmark since the 1960s (or earlier) for females and the 1990s for males.…”

“…How to find the most relevant fitting period remains an open question. Generally, a longer fitting period provides more accurate forecasts, but it is also important to select a fitting period that reflects an ongoing or emerging dynamic (Bergeron-Boucher et al 2019b;Janssen and Kunst 2007). The year when M starts to increase, capturing the mortality postponement dynamic, differs across countries and sexes.…”