Lee–Carter Mortality Forecasting: A Parallel Generalized Linear Modelling Approach for England and Wales Mortality Projections

Renshaw, A. E.; Haberman, Steven

doi:10.1111/1467-9876.00393

Cited by 157 publications

(124 citation statements)

References 23 publications

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“…Bongaarts (2005) uses as a reference the LeeCarter method without adjustment. Actual rates may be replaced by the average observed rates over the last two or three years of the fitting period (Renshaw and Haberman, 2003a). …”

Section: Discussionmentioning

confidence: 99%

Lee-Carter mortality forecasting: a multi-country comparison of variants and extensions

Booth¹,

Hyndman²,

Tickle³

et al. 2006

DemRes

212

180

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We compare the short-to medium-term accuracy of five variants or extensions of the Lee-Carter method for mortality forecasting. These include the original Lee-Carter, the Lee-Miller and Booth-Maindonald-Smith variants, and the more flexible Hyndman-Ullah and De Jong-Tickle extensions. These methods are compared by applying them to sexspecific populations of 10 developed countries using data for 1986-2000 for evaluation. All variants and extensions are more accurate than the original Lee-Carter method for forecasting log death rates, by up to 61%. However, accuracy in log death rates does not necessarily translate into accuracy in life expectancy. There are no significant differences among the five methods in forecast accuracy for life expectancy.

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Section: Discussionmentioning

confidence: 99%

Lee-Carter mortality forecasting: a multi-country comparison of variants and extensions

Booth¹,

Hyndman²,

Tickle³

et al. 2006

DemRes

212

180

View full text Add to dashboard Cite

show abstract

“…Booth et al (2002) and Renshaw and Haberman (2003b) examine the use of more than one term. Parallel approaches within the GLM framework have also been developed (Renshaw and Haberman, 2003c). Stochastic population forecasts using functional data models for mortality, fertility and migration following the functional data paradigm, propose smoothing the mortality curves for each year using constrained regression splines prior to fitting a model using principal components decomposition.…”

Section: Mortalitymentioning

confidence: 99%

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

Hyndman

Booth

2008

International Journal of Forecasting

177

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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“…Since Lee and Carter proposed dynamic life tables in 1992 and use them to study projections of the mortality rate in US (Lee and Carter, 1992), there have been several papers forecasting population mortality in other developed countries such as Canada (Nault, 1993), Chile (Lee and Rofman, 1994), Japan (Wilmoth, 1996), Belgium (Brouhns et al, 2002), Austria (Carter and Prkawetz, 2001), England and Wales (Renshaw and Haberman, 2003a), Australia (Booth and Tickle, 2003) and Spain (Guillen and Vidiella-i-Anguera, 2005). This paper applies the Lee-Carter model with different methods for estimating parameters, compares all these approaches using mortality data for Spain during the 1980-1999 period, paying attention to the analysis and study of residuals, and predicts death probabilities and expected remaining lifetime for future years.…”

Section: Introductionmentioning

confidence: 99%

Modelling and forecasting mortality in Spain

Debón

Montes

Puig

2008

European Journal of Operational Research

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Experience shows that static life tables overestimate death probabilities. As a consequence of this overestimation the premiums for annuities, pensions and life insurance are not what they actually should be, with negative effects for insurance companies or policy-holders. The reason for this overestimation is that static life tables, through being computed for a specific period of time, cannot take into account the decreasing mortality trend over time. Dynamic life tables overcome this problem by incorporating the influence of the calendar when graduating mortality. Recent papers on the topic look for the development of new methods to deal with this dynamism.Most methods used in dynamic tables are parametric, apply traditional mortality laws and then analyze the evolution of estimated parameters with time series techniques. Our contribution consists in extending and applying Lee-Carter methods to Spanish mortality data, exploring residuals and future trends.

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Lee–Carter Mortality Forecasting: A Parallel Generalized Linear Modelling Approach for England and Wales Mortality Projections

Cited by 157 publications

References 23 publications

Lee-Carter mortality forecasting: a multi-country comparison of variants and extensions

Lee-Carter mortality forecasting: a multi-country comparison of variants and extensions

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

Modelling and forecasting mortality in Spain

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