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Erschienen in: Zeitschrift für die gesamte Versicherungswissenschaft 5/2017

01.12.2017 | Abhandlung

A principal component model for forecasting age- and sex-specific survival probabilities in Western Europe

verfasst von: Patrizio Vanella

Erschienen in: Zeitschrift für die gesamte Versicherungswissenschaft | Ausgabe 5/2017

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Abstract

The assessment of future mortality is of high importance in many areas where the allocation of future resources has to be planned in time, especially in social security and private life insurance. This contribution represents an extension of the classic forecasting approaches of Bell–Monsell and Lee–Carter. Based on a forecast of the first two principal components, age- and sex-specific survival probabilities for 18 Western European countries are predicted simultaneously until the year 2070. In addition to the correlations in the mortality trends between the age groups and the genders, international trends in mortality are captured as well. A major improvement in the classic Lee–Carter models is the adequate quantification of the uncertainty associated with the whole system of variables by stochastic simulation of all remaining principal components with simple time series models. The model’s easy applicability to further analyses is illustrated by forecasting the median life span as well as the resulting Gender Gap for Germany, France, and Italy.

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Fußnoten
1
See Bell and Monsell (1991) as well as Lee and Carter (1992) for a detailed description of the respective models.
 
2
Germany, France, Italy, England with Wales, Spain, the Netherlands, Belgium, the Czech Republic, Portugal, Hungary, Sweden, Austria, Denmark, Finland, Slovakia, Scotland, Switzerland and Norway.
 
3
See Wooldridge (2013) for a more detailed description of the optimization technique.
 
4
An age-specific survival probability of zero would mean that a person has no chance of surviving the coming year, which might be rejected if there is no age limit. An age-specific survival probability of one would mean no risk of death at all, which is clearly impossible.
 
5
The logit of a variable x is defined as \(ln\left (\frac{x}{1-x}\right )\).
 
6
Handl (2010) gives a nice and thorough overview of the common criteria; Vanella (2017) illustrates the application for age-specific demographic measures.
 
7
Detailed descriptions of the tests and measures are given by Shumway and Stoffer (2011) and Vanella (2017).
 
8
Note that 1000 simulations is a very small number in this context, which is due to keeping the huge number of variables and the long forecasting horizon computable.
 
9
\(\mathrm{logit}^{-1}\) denotes the inverse standard logistic transformation \(\frac{\exp \left (x\right )}{1+\exp \left (x\right )}\) for some x.
 
10
The MLS here is defined by the age, at which half of the population of a certain cohort would be deceased, assuming a constant mortality level similar to the respective period.
 
11
This is restricted here to the three mentioned countries to keep it short, but the interpretation may well be translated to the other countries as well, given that the coefficients are quite similar.
 
12
See Shumway and Stoffer (2011) for a more detailed definition of the AIC.
 
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Metadaten
Titel
A principal component model for forecasting age- and sex-specific survival probabilities in Western Europe
verfasst von
Patrizio Vanella
Publikationsdatum
01.12.2017
Verlag
Springer Berlin Heidelberg
Erschienen in
Zeitschrift für die gesamte Versicherungswissenschaft / Ausgabe 5/2017
Print ISSN: 0044-2585
Elektronische ISSN: 1865-9748
DOI
https://doi.org/10.1007/s12297-017-0393-y

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