The impact of the choice of life table statistics when forecasting mortality

Marie-Pier Bergeron-Boucher; Søren Kjærgaard; James E. Oeppen; James W. Vaupel

Volume 41 - Article 43 | Pages 1235–1268

The impact of the choice of life table statistics when forecasting mortality

By Marie-Pier Bergeron-Boucher, Søren Kjærgaard, James E. Oeppen, James W. Vaupel

Date received:

5 Mar 2019

Date published:

14 Nov 2019

Word count:

5645

Keywords:

life expectancy, life table, mortality forecasts, rate of mortality improvement

DOI:

10.4054/DemRes.2019.41.43

Additional Files:

demographic-research.41-43 (zip file, 3 kB)
readme.41-43 (text file, 1 kB)

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Abstract

Background: Different ways to forecast mortality have been suggested, with many forecasting models based on the extrapolation of age-speciﬁc death rates. Recent studies, however, have looked into forecasting models based on other mortality indicators, such as life expectancy or life table deaths.

Objective: Here we ask, what are the implications of choosing one indicator over another to forecast mortality?

Methods: We compare ﬁve extrapolative models based on different life table statistics: death rates, death probabilities, survival probabilities, life table deaths, and life expectancy at birth. We show the consequences of using a speciﬁc indicator for the forecast results by looking into time changes in the indicators produced by the models.

Results: The results show that forecasting based on death rates and probabilities of death leads to more pessimistic forecasts than using survival probabilities, life table deaths, and life expectancy when applying existing models based on linear extrapolation of (transformed) indicators.

Contribution: The paper raises awareness that the use of a speciﬁc life table statistic as input for mortality forecasting has a signiﬁcant impact on the forecast results.