A comparative study of parametric mortality projection models

Haberman, Steven; Renshaw, A. E.

doi:10.1016/j.insmatheco.2010.09.003

Cited by 153 publications

(164 citation statements)

References 23 publications

(41 reference statements)

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“…3.5 Back-testing and life expectancy and annuity predictions In this section, we conduct a back-testing exercise along the lines of Dowd et al (2010) and Haberman and Renshaw (2011) and focus on life expectancy and annuity predictions. We use 2 approaches as in Haberman and Renshaw (2011).…”

Section: Model Dynamicsmentioning

confidence: 99%

Modelling and projecting mortality improvement rates using a cohort perspective

Haberman

Renshaw

2013

Insurance: Mathematics and Economics

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This is the accepted version of the paper.This version of the publication may differ from the final published version. Abstract: We investigate the feasibility of defining, modelling and projecting of (scaled) mortality improvement rates along cohort years-of-birth; that is, using a cohort perspective. This is in contrast to the approach in the literature which has considered mortality improvement rates that are defined by reference to changes in mortality rates over successive calendar years, that is, using a period perspective. In this paper, we offer a comparison of the 2 parallel approaches to modelling and forecasting using mortality improvement rates. Comparisons of simulated life expectancy and annuity value predictions (mainly by the cohort method) using the England & Wales population mortality experiences for males and females under a variety of controlled data trimming exercises are presented and comparisons are also made between the parallel cohort and period based approaches. Permanent repository link

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Section: Model Dynamicsmentioning

confidence: 99%

Modelling and projecting mortality improvement rates using a cohort perspective

Haberman

Renshaw

2013

Insurance: Mathematics and Economics

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“…There is a vast literature which investigates this component of aggregate mortality modelling starting with the seminal work of Lee and Carter (1992); in addition, some overviews are provided by e.g. Cairns et al (2011) and Haberman and Renshaw (2011). The nature of our data suggests the exclusion of any overt cohort covariate.…”

Section: The Regression Formulamentioning

confidence: 99%

Modelling Cause-of-Death Mortality and the Impact of Cause-Elimination

2013

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The analysis of causal mortality provides rich insight into changes in mortality trends that are hidden in population level data. Therefore, we develop and apply a multinomial logistic framework to model causal mortality. We use internationally classified cause-of-death categories and data obtained from the World Health Organization. Inherent dependence amongst the competing causes is accounted for in the framework, which also allows us to investigate the effects of improvements in, or the elimination of, cause-specific mortality. This has applications to scenario-based forecasting often used to assess the impact of changes in mortality. The multinomial model is shown to be more conservative than commonly used approaches based on the force of mortality. We use the model to demonstrate the impact of cause-elimination on aggregate mortality using residual life expectancy and apply the model to a French case study.

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“…The PD plays an important role in actuarial Science include: models for pensioner mortality data [2], parametric mortality projection models [3].…”

Section: Introductionmentioning

confidence: 99%

Comparison between Bayesian and Maximum Likelihood Methods for parameters and the Reliability function of Perks Distribution

Al-Sultany

Mohammed

2018

ijs

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In this paper, we have derived Bayesian estimation for the parameters and reliability function of Perks distribution based on two different loss functions, Lindley's approximation has been used to obtain those values. It is assumed that the parameter behaves as a random variable have a Gumbell Type  prior with noninformative is used. And after the derivation of mathematical formulas of those estimations, the simulation method was used for comparison depending on mean square error (MSE) values and integrated mean absolute percentage error (IMAPE) values respectively. Among of conclusion that have been reached, it is observed that, the LE-NR estimate introduced the best perform for estimating the parameter λ.

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A comparative study of parametric mortality projection models

Cited by 153 publications

References 23 publications

Modelling and projecting mortality improvement rates using a cohort perspective

Modelling and projecting mortality improvement rates using a cohort perspective

Modelling Cause-of-Death Mortality and the Impact of Cause-Elimination

Comparison between Bayesian and Maximum Likelihood Methods for parameters and the Reliability function of Perks Distribution

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