Impact of different mortality forecasting methods and explicit assumptions on projected future life expectancy: The case of the Netherlands

Stoeldraijer, Lenny; Duin, Coen van; Wissen, van Leonardus; Janssen, Fanny

doi:10.4054/demres.2013.29.13

Cited by 73 publications

(68 citation statements)

References 36 publications

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“…Even though many scientists have demonstrated crucial differences between mortality scenarios in terms of life expectancy at birth (often a scenario variable in population projections) and predicted death rates (Bell 1997;Benjamin and Soliman 1993;Cairns et al 2011;Janssen and Kunst 2007;Pollard 1987;Shang et al 2011;Stoeldraijer et al 2013) the impact on projected population ageing is rarely studied. An infinite number of age-specific mortality patterns -with potentially different consequences for population ageing -may produce the same trajectory of life expectancy at birth.…”

Section: Introductionmentioning

confidence: 99%

“…In practice, the above-described methods are used in different combinations: one method is used to produce scenarios for life expectancy at birth and another method is used to disaggregate that scenario into mortality by age-sex. For more detailed reviews of the mortality models see Booth and Tickle (2008) and Ishii (2014), and Stoeldraijer et al (2013) for a more recent review of current practices in Europe.…”

Section: Introductionmentioning

confidence: 99%

“…As life expectancy increases and population-ageing speeds up worldwide (Lutz et al 2008), a considerable effort is being made to expand the methodology of mortality projection (Booth and Tickle 2008;Ediev 2011;Lee and Carter 1992;Mayhew and Smith 2013;Pollard 1987;Raftery et al 2012;Stoeldraijer et al 2013). However, authors rarely pay attention to the importance of choosing mortality models and their implications for assessing future population size and composition, and consequently indicators of population ageing.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Does selection of mortality model make a difference in projecting population ageing?

Scherbov¹,

Ediev²

2016

DemRes

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Does selection of mortality model make a difference in projecting population ageing?

Scherbov¹,

Ediev²

2016

DemRes

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“…Apart from being studied in academic literature, the multi-population approach recently found its way to official census projections. For example in the Netherlands, the Central Bureau of Statistics ([CBS]) 2 uses the Li and Lee (2005) ([LL]) model for Dutch data, combined with data from ten other European countries 3 , for its official 2012-2060 mortality projection (see Stoeldraijer et al (2013a) and Stoeldraijer et al (2013b)). The 2014 projection of the Royal Dutch Actuarial Association 4 also uses the model of Li and Lee (2005) on a collection of European countries, as described in Koninklijk Actuarieel Genootschap (2014).…”

Section: Introductionmentioning

confidence: 99%

Bayesian Poisson Log-Bilinear Models for Mortality Projections with Multiple Populations

2015

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Life insurers, pension funds, health care providers and social security institutions face increasing expenses due to continuing improvements of mortality rates. The actuarial and demographic literature has introduced a myriad of (deterministic and stochastic) models to forecast mortality rates of single populations. This paper presents a Bayesian analysis of two related multi-population mortality models of log-bilinear type, designed for two or more populations. Using a larger set of data, multi-population mortality models allow joint modelling and projection of mortality rates by identifying characteristics shared by all subpopulations as well as sub-population specific effects on mortality. This is important when modeling and forecasting mortality of males and females, regions within a country and when dealing with index-based longevity hedges. Our first model is inspired by the two factor Lee & Carter model of Renshaw and Haberman (2003) and the common factor model of Carter and Lee (1992). The second model is the augmented common factor model of Li and Lee (2005). This paper approaches both models in a statistical way, using a Poisson distribution for the number of deaths at a certain age and in a certain time period. Moreover, we use Bayesian statistics to calibrate the models and to produce mortality forecasts. We develop the technicalities necessary for Markov Chain Monte Carlo ([MCMC]) simulations and provide software implementation (in R) for the models discussed in the paper. Key benefits of this approach are multiple. We jointly calibrate the Poisson likelihood for the number of deaths and the times series models imposed on the time dependent parameters, we enable full allowance for parameter uncertainty and we are able to handle missing data as well as small sample populations. We compare and contrast results from both models to the results obtained with a frequentist single population approach and a least squares estimation of the augmented common factor model.

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“…Keyfitz (1991) compared results from the Geometric and Brass methods as well as from a number of fitted mathematical models. Stoeldraijer, van Duin, van Wissen, and Janssen (2013) compared the life expectancy produced by different mortality forecasting methods. Neither study considered the accuracy of the methods.…”

Section: Projected Very Elderly Population Numbersmentioning

confidence: 99%

Population estimates and projections for Australia’s very elderly population at state and national level

Terblanche¹

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As in many developed countries, the very elderly population (ages 85+) is the fastest growing age group in Australia, with far-reaching economic and social consequences. To effectively plan and budget for the income, aged care and health care needs of the very elderly, accurate estimates and projections are required. There are, however, several obstacles relating to the availability and accuracy of very elderly data. Official population estimates at very high ages in Australia have been found to be too high and fluctuate implausibly over time. International and local studies have found large errors in projected very elderly populations, stemming from inaccurate mortality rate forecasts.This thesis aims to create accurate estimates and projections for Australia's very elderly population at a state and national level. Various methods for estimating very elderly populations from death counts were assessed for accuracy at both a national and state level.While the Human Mortality Database uses such methods to create estimates for many countries, their accuracy has never been assessed for Australia. Furthermore, little is known, locally or internationally, about their performance at a sub-national scale. In this study, the accuracy of various nearly-extinct-cohort methods were assessed at the Australian national and state level by retrospectively applying them to extinct cohorts and comparing the results against those obtained from applying the Extinct Cohort method. Suitable methods were applied to create very elderly population estimates and death rates for Australia from 1972-2012 by sex, state and at single ages 85-110+. The growth, changes in the age-sex composition and the demographic drivers of growth of Australia's very elderly population were analysed. Based on these estimates, more reliable death rates were calculated, allowing a detailed study of the changing patterns and trends in Australian adult mortality. Finally, a number of mortality forecasting methods were retrospectively evaluated for their accuracy in projecting adult death rates for Australia over 10 and 20 years ending in 2012. An appropriate method was applied to create projections for the next three decades.It was found that, compared to the official census-based estimates, more plausible and accurate estimates, especially for ages 95+, could be derived from death counts. The Survivor Ratio (SR) method with results constrained to official estimates for ages 85+ produced accurate very elderly population estimates for Australia across the sexes and ages at both a national and state level. Internal migration is sufficiently minor to be ignored. Very accurate state-level estimates could also be derived using a simpler method of apportioning national single-age estimates between the states. Adult death rates in Australia were found to show consistent and regular patterns of decline since the 1970s, with rates of decline decreasing with age. These patterns support the use of simple direct extrapolation methods for forecasting. The Geometric,...

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Impact of different mortality forecasting methods and explicit assumptions on projected future life expectancy: The case of the Netherlands

Cited by 73 publications

References 36 publications

Does selection of mortality model make a difference in projecting population ageing?

Does selection of mortality model make a difference in projecting population ageing?

Bayesian Poisson Log-Bilinear Models for Mortality Projections with Multiple Populations

Population estimates and projections for Australia’s very elderly population at state and national level

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