Bevölkerungsdynamik und Nachhaltiges Wohlbefinden

Auf einen Blick Projekte Publikationen Team


Forecasting Population-Level Mortality

Ugofilippo Basellini; in Zusammenarbeit mit Carlo Giovanni Camarda (French National Institute for Demographic Studies, Paris, Frankreich)

Ausführliche Beschreibung

Mortality modeling and forecasting play a central role in demographic and actuarial analyses. Renewed and increased attention to this area of research has been stimulated recently by two pressing challenges faced by modern societies: population aging and longevity risk. Virtually every country of the world is seeing population aging increases as a result of continuous declines in mortality and fertility. Furthermore, unanticipated improvements in longevity have generated an enormous global longevity risk market.

The last few decades have witnessed significant advances in mortality forecasting, including the shift from deterministic to stochastic approaches. Widely used methods have repeatedly failed to anticipate the sustained rate of mortality improvements observed in many low-mortality countries, however. As such, the need for innovative models that can predict the future course of mortality more accurately than previous approaches is evident and timely.

We introduce innovative statistical methods that can provide novel insights into the analysis and forecast of human mortality. The key measure used is age-at-death distribution, a very useful measure of age-specific mortality. Despite being well suited for analyzing mortality developments over age and time, only a few efforts have been made to leverage age-at-death distributions for modeling and forecasting mortality. As such, we propose the use of age-at-death distribution coupled with methods of smoothing and functional analysis in order to obtain a broader perspective of mortality developments and more accurate mortality forecasts. Generally, the use of age-at-death distribution results in more optimistic mortality forecasts than those obtained with methodologies based on death rates, such as the Lee-Carter method.

We will also develop methodologies that enable researchers to decompose age patterns of mortality projections into childhood, early adulthood, and senescent components. This decomposition provides additional insights into the drivers of mortality forecasts and enables us to assess the relative contribution of each component to the observed and forecasted changes in longevity. We proposed two methods to achieve this goal: one based on age-at-death distribution (Basellini and Camarda 2020) and the other based on the Lee-Carter methodology (Camarda and Basellini 2021).

Moreover, we are pursuing the development of methodologies for cohort mortality forecasts, i.e., for completing the mortality experience of real birth cohorts. To test the goodness-of-fit and the forecast accuracy of our proposed methodologies, we start by focusing on mortality changes in high-longevity populations that are characterized by higher data quality. Once the methodologies will be fully tested and validated, we expand our analyses to countries that have lower data quality and atypical mortality developments. 

Beobachtete und prognostizierte Sterberaten für Schweizer Männer

Actual, estimated and forecast death rates by Three-Component smooth Lee-Carter model over age (left) and time (right) on a log scale for Swiss males. Unlike in the original Lee-Carter method, mortality forecasts over ages are smooth, and rates of mortality improvement over time are not constant. © Camarda and Basellini (2021)


Alterung, Sterblichkeit und Langlebigkeit, Projektionen und Vorhersagen, Statistik und Mathematik

Schlagworte (Region):

Europa, Japan, Vereinigte Staaten


Basellini, U.; Camarda, C. G.; Booth, H.:
International Journal of Forecasting 39:3, 1033–1049. (2023)    
Camarda, C. G.; Basellini, U.:
European Journal of Population 37:3, 569–602. (2021)
Basellini, U.; Camarda, C. G.:
In: Developments in demographic forecasting, 105–129. Cham: Springer International Publishing. (2020)    
Basellini, U.; Kjærgaard, S.; Camarda, C. G.:
Insurance: Mathematics and Economics 91, 129–143. (2020)
Bergeron Boucher, M.-P.; Kjærgaard, S.; Pascariu, M. D.; Aburto, J. M.; Alvarez Martinez, J. A.; Basellini, U.; Rizzi, S.; Vaupel, J. W.:
In: Developments in demographic forecasting, 131–151. Cham: Springer International Publishing. (2020)    
Pascariu, M. D.; Basellini, U.; Aburto, J. M.; Canudas-Romo, V.:
Risks 8:4, 109.1–109.18. (2020)    
Das Max-Planck-Institut für demografische Forschung (MPIDR) in Rostock ist eines der international führenden Zentren für Bevölkerungswissenschaft. Es gehört zur Max-Planck-Gesellschaft, einer der weltweit renommiertesten Forschungsgemeinschaften.