Stochastic modelling and projection of mortality improvements using a hybrid parametric/semi-parametric age–period–cohort model

Dodd, Erengul; Forster, Jonathan J.; Bijak, Jakub; Smith, Peter

doi:10.1080/03461238.2020.1815238

Cited by 8 publications

(5 citation statements)

References 25 publications

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“…Aside from the observed mortality rates, expert opinions on future mortality rates collected from external sources can be valuable information in modeling and projecting mortality rates. In the literature, many studies have considered incorporating external information in mortality modeling, for example, Billari et al [ 1 ], Billari et al [ 2 ], Dowd et al [ 14 ] and Dodd et al [ 13 ]. In this paper, we propose an alternative approach that is compatible with the cubic B-spline MI model, and also in line with the needs of the insurance industry’s practice.…”

Section: The Proposed Modelmentioning

confidence: 99%

“…Dowd et al [ 14 ] proposed to deliberately alter the drift values of the period-specific factors. Dodd et al [ 13 ] considered using a weighted average method to blend the external rates and internal results. Moreover, although factor-based models can provide stochastic uncertainty to the projected rates, uncertainty surrounding the external rates cannot be easily measured.…”

Section: Introductionmentioning

confidence: 99%

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Smooth projection of mortality improvement rates: a Bayesian two-dimensional spline approach

Zhu

Zhou

2022

Eur. Actuar. J.

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This paper proposes a spline mortality model for generating smooth projections of mortality improvement rates. In particular, we follow the two-dimensional cubic B-spline approach developed by Currie et al. (Stat Model 4(4):279–298, 2004), and adopt the Bayesian estimation and LASSO penalty to overcome the limitations of spline models in forecasting mortality rates. The resulting Bayesian spline model not only provides measures of stochastic and parameter uncertainties, but also allows external opinions on future mortality to be consistently incorporated. The mortality improvement rates projected by the proposed model are smoothly transitioned from the historical values with short-term trends shown in recent observations to the long-term terminal rates suggested by external opinions. Our technical work is complemented by numerical illustrations that use real mortality data and external rates to showcase the features of the proposed model.

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Section: The Proposed Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Smooth projection of mortality improvement rates: a Bayesian two-dimensional spline approach

Zhu

Zhou

2022

Eur. Actuar. J.

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“…In order to improve the dynamical model (1), which was proposed in [10], in this work, we introduce the history using the concept of Improvement Rate (see [37][38][39][40]). These rates characterize the evolution of the mortality year-to-year or between two arbitrary moments, t 0 and t 1 .…”

Section: Dynamical Kernel Graduation With Delaymentioning

confidence: 99%

On a Retarded Nonlocal Ordinary Differential System with Discrete Diffusion Modeling Life Tables

Morillas

Valero

2021

Mathematics

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In this paper, we consider a system of ordinary differential equations with non-local discrete diffusion and finite delay and with either a finite or an infinite number of equations. We prove several properties of solutions such as comparison, stability and symmetry. We create a numerical simulation showing that this model can be appropriate to model dynamical life tables in actuarial or demographic sciences. In this way, some indicators of goodness and smoothness are improved when comparing with classical techniques.

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“…A similar approach within a Bayesian framework would have to consider that using expert opinion about future rates is different from the standard approach of eliciting information about model parameters directly. Work in Dodd et al (2018b) describes one way in which this could be achieved. Beyond this, there are also opportunities to investigate the possibility of extending similar methods to other demographic components, particularly fertility.…”

Section: Life Expectancymentioning

confidence: 99%

Projecting UK Mortality by Using Bayesian Generalized Additive Models

Hilton

Dodd

Forster

et al. 2018

Journal of the Royal Statistical Society Series C: Applied Statistics

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Summary Forecasts of mortality provide vital information about future populations, with implications for pension and healthcare policy as well as for decisions made by private companies about life insurance and annuity pricing. The paper presents a Bayesian approach to the forecasting of mortality that jointly estimates a generalized additive model (GAM) for mortality for the majority of the age range and a parametric model for older ages where the data are sparser. The GAM allows smooth components to be estimated for age, cohort and age‐specific improvement rates, together with a non‐smoothed period effect. Forecasts for the UK are produced by using data from the human mortality database spanning the period 1961–2013. A metric that approximates predictive accuracy is used to estimate weights for the ‘stacking’ of forecasts from models with different points of transition between the GAM and parametric elements. Mortality for males and females is estimated separately at first, but a joint model allows the asymptotic limit of mortality at old ages to be shared between sexes and furthermore provides for forecasts accounting for correlations in period innovations.

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Stochastic modelling and projection of mortality improvements using a hybrid parametric/semi-parametric age–period–cohort model

Abstract: Stochastic modelling and projection of mortality improvements using a hybrid parametric/semi-parametric age-period-cohort model, Scandinavian Actuarial Journal,

Cited by 8 publications

References 25 publications

Smooth projection of mortality improvement rates: a Bayesian two-dimensional spline approach

Smooth projection of mortality improvement rates: a Bayesian two-dimensional spline approach

On a Retarded Nonlocal Ordinary Differential System with Discrete Diffusion Modeling Life Tables

Projecting UK Mortality by Using Bayesian Generalized Additive Models

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