The Changing Shape of English Life Tables

Forfar, David O.; Smith, Donald M.

doi:10.1017/s0071368600009137

Cited by 39 publications

(19 citation statements)

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“…A more parsimonious approach is to forecast the parameters of a 'law' of mortality representing the age pattern, also ensuring regularity. Among the numerous existing models, the eight-parameter Heligman-Pollard (1980) model and the multi-exponential model have been used in forecasting with limited success (McNown and Rogers, 1989;McNown et al, 1995;Forfar and Smith, 1987). Interdependencies among parameters call for multivariate ARIMA models.…”

Section: Mortalitymentioning

confidence: 99%

Stochastic population forecasts using functional data models for mortality, fertility and migration

Hyndman

Booth

2008

International Journal of Forecasting

176

151

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Age-sex-specific population forecasts are derived through stochastic population renewal using forecasts of mortality, fertility and net migration. Functional data models with time series coefficients are used to model age-specific mortality and fertility rates. As detailed migration data are lacking, net migration by age and sex is estimated as the difference between historic annual population data and successive populations one year ahead derived from a projection using fertility and mortality data. This estimate, which includes error, is also modeled using a functional data model. The three models involve different strengths of the general BoxCox transformation chosen to minimise out-of-sample forecast error. Uncertainty is estimated from the model, with an adjustment to ensure the one-step-forecast variances are equal to those obtained with historical data. The three models are then used in the Monte Carlo simulation of future fertility, mortality and net migration, which are combined using the cohort-component method to obtain age-specific forecasts of the population by sex. The distribution of forecasts provides probabilistic prediction intervals. The method is demonstrated by making 20-year forecasts using Australian data for the period 1921-2003.

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Section: Mortalitymentioning

confidence: 99%

Stochastic population forecasts using functional data models for mortality, fertility and migration

Hyndman

Booth

2008

International Journal of Forecasting

176

151

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“…Forfar and Smith (1988) have fitted the HeligmanPollard curve (3.7) to English life tables ELT1 to ELT13, for both males and females, and then have analyzed the behavior of the relevant parameters. A further fitting has been performed using the law obtained replacing the third term in (3.7) with GH x /(1 + GH x ), and then mortality forecasts have been calculated.…”

Section: Parametric Modelsmentioning

confidence: 99%

Survival models in a dynamic context: a survey

Pitacco

2004

Insurance: Mathematics and Economics

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“…In addition the variability of the curve itself can be measured by constructing appropriate confidence bands for the entire mortality curve. Heligman and Pollard (1980), Hartmann (1987), Forfar and Smith (1987), Kostaki (1992Kostaki ( , 2000 fit the HP model to empirical data sets of several countries and different time periods. In these investigations, the parameters of the model are estimated using an iterative routine of the NAG library that is based upon a modification of the Gauss-Newton algorithm.…”

Section: Laws Of Mortalitymentioning

confidence: 99%

Bootstrap Techniques for Mortality Models

Karlis¹,

Kostaki²

2002

Biom. J.

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Biostatisticians, actuaries and demographers are interested in accurately finding the age specific mortality pattern of a population. Certain different approaches have been proposed in the literature for representing the mortality of a population. Among them laws of mortality assuming a specific functional parametrization for the mortality rates have become popular in recent years mainly because of the existence of computers to handle the vast number of computations needed. The uncertainty of such a functional representation has been overlooked. The researcher is interested in both the uncertainty on the parameters and the uncertainty on the curve itself. The former provides information for specific parts of the curve that directly correspond to certain parameters while the latter allows for comparisons over time or space. Manipulation of the uncertainty can be very helpful for prediction purposes. A bootstrap approach is described, as an alternative to standard inferential methods based on asymptotic standard error theory. Such an approach can provide standard errors for both the parameters and the curve as well as it can be used for direct comparison of different curves over time or space. Application to empirical data from Sweden is also provided.

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The Changing Shape of English Life Tables

Abstract: 1. In a paper to the Institute of Actuaries (JIA 107, p. 49) Heligman and Pollard fitted certain mathematical curves to Australian national mortality. They showed that a good fit could be obtained throughout the whole of life with the following curve:

Cited by 39 publications

References 1 publication

Stochastic population forecasts using functional data models for mortality, fertility and migration

Stochastic population forecasts using functional data models for mortality, fertility and migration

Survival models in a dynamic context: a survey

Bootstrap Techniques for Mortality Models

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