A Semiparametric Panel Approach to Mortality Modeling

2017

Journal of Forecasting

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Temperature changes are known to affect the social and environmental determinants of health in various ways. Consequently, excess deaths as a result of extreme weather conditions may increase over the coming decades because of climate change. In this paper, the relationship between trends in mortality and trends in temperature change (as a proxy) is investigated using annual data and for specified (warm and cold) periods during the year in the UK. A thoughtful statistical analysis is implemented and a new stochastic, central mortality rate model is proposed. The new model encompasses the good features of the Lee and Carter (Journal of the American Statistical Association, 1992, 87: 659–671) model and its recent extensions, and for the very first time includes an exogenous factor which is a temperature‐related factor. The new model is shown to provide a significantly better‐fitting performance and more interpretable forecasts. An illustrative example of pricing a life insurance product is provided and discussed.

Section: Extrapolative Mortality Modelingmentioning

confidence: 99%

Mortality effects of temperature changes in the United Kingdom

Seklecka

Pantelous

2017

Journal of Forecasting

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“…Following the investigations of O'Hare and Li (2012) and Li et al (2015), to statistically assess the fit quality of the proposed model, we define the following measures:…”

Section: Fitting Resultsmentioning

confidence: 99%

“…A suitable multivariate stochastic process is fitted to time series .Ä 1 t ; Ä 2 t ; Ä 3 t / for forecasting purposes. A recent study by Li et al (2015) argued that the time effects in stochastic mortality models could actually be smooth functions of time since the nonparametric smoothers of those time effects show clear parametric patterns. Therefore, stochastic mortality models can be considered as a special case of the Renshaw et al (1996) model.…”

Section: Review Of Existing Mortality Modelsmentioning

confidence: 99%

“…In Renshaw et al (1996) study, they restrict the highest degree of Legendre orthogonal polynomials of age to be 6. In another study on mortality modeling by Li et al (2015), it is found that the optimal order of polynomials of time is at most 6 for a range of developed countries. Therefore, in this paper, we will consider Legendre orthogonal polynomials up to 6th order for both x 0 and t 0 .…”

Section: Legendre Orthogonal Polynomialsmentioning

confidence: 99%

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A Flexible Functional Form Approach To Mortality Modeling: Do We Need Additional Cohort Dummies?

Han

Vahid

2016

Journal of Forecasting

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The increasing amount of attention paid to longevity risk and funding for old age has created the need for precise mortality models and accurate future mortality forecasts. Orthogonal polynomials have been widely used in technical fields and there have also been applications in mortality modeling. In this paper we adopt a flexible functional form approach using two‐dimensional Legendre orthogonal polynomials to fit and forecast mortality rates. Unlike some of the existing mortality models in the literature, the model we propose does not impose any restrictions on the age, time or cohort structure of the data and thus allows for different model designs for different countries' mortality experience. We conduct an empirical study using male mortality data from a range of developed countries and explore the possibility of using age–time effects to capture cohort effects in the underlying mortality data. It is found that, for some countries, cohort dummies still need to be incorporated into the model. Moreover, when comparing the proposed model with well‐known mortality models in the literature, we find that our model provides comparable fitting but with a much smaller number of parameters. Based on 5‐year‐ahead mortality forecasts, it can be concluded that the proposed model improves the overall accuracy of the future mortality projection. Copyright © 2016 John Wiley & Sons, Ltd.

“…There have been a number of studies on the comparison of the forecasting performances of different models (Cairns et al 2011;Haberman and Renshaw 2009;Hyndman and Ullah 2007), and the quantitative and qualitative criteria used include: the overall accuracy; allowance for cohort effect; biological reasonableness; and the robustness of forecast. However, as far as we know, very few studies have considered the question of whether a mortality model should treat past and recent mortality experience equally in the forecasting process (see the discussions in Li et al (2015Li et al ( , 2016).…”

Section: Introductionmentioning

confidence: 99%

Mortality Forecasting: How Far Back Should We Look in Time?

2019

Risks

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Extrapolative methods are one of the most commonly-adopted forecasting approaches in the literature on projecting future mortality rates. It can be argued that there are two types of mortality models using this approach. The first extracts patterns in age, time and cohort dimensions either in a deterministic fashion or a stochastic fashion. The second uses non-parametric smoothing techniques to model mortality and thus has no explicit constraints placed on the model. We argue that from a forecasting point of view, the main difference between the two types of models is whether they treat recent and historical information equally in the projection process. In this paper, we compare the forecasting performance of the two types of models using Great Britain male mortality data from 1950–2016. We also conduct a robustness test to see how sensitive the forecasts are to the changes in the length of historical data used to calibrate the models. The main conclusion from the study is that more recent information should be given more weight in the forecasting process as it has greater predictive power over historical information.