Mortality Forecasting and Trend Shifts: an Application of the Lee-Carter Model to Swedish Mortality Data*

2007 ◽  
Vol 72 (1) ◽  
pp. 37-50 ◽  
Author(s):  
Hans Lundström ◽  
Jan Qvist
Keyword(s):  
Mortality Data ◽  
Mortality Forecasting ◽  
Carter Model

scholarly journals Modelling and Prediction of the Gross Mortality Rate in Ecuador

2018 ◽  
Vol 6 (3) ◽  
Author(s):  
Mónica Mite ◽  
Sandra Garcia-Bustos ◽  
Marcela Pincay ◽  
Ana Debón ◽  
Francisco Santoja
Keyword(s):  
Mortality Rate ◽  
Cohort Effect ◽  
Mortality Data ◽  
Arima Models ◽  
Men And Women ◽  
Mortality Forecasting ◽  
Basic Model ◽  
Adjusted Model ◽  
Age Range ◽  
Carter Model

This paper presents the results obtained from the modelling of the mortality data in Ecuador from 1990 to 2010, using the StMoMo library in the open source programming language R. This library was developed based on the Generalized Age-Period-Cohort Models (GAPC), among which is the Lee-Carter model, which has been widely applied in the actuarial area. The gross mortality rate of men and women in an age range of 1 to 85 years was modelled for the data of Ecuador, in the period 1990-2010. Of a total of eight models, two models have been selected because they present a good fit of the data for both genders. The first is the basic model of Lee-Carter and the second, the Plat model, which incorporates the cohort effect. A comparison was made with the two models to determine which one has a better forecast in a horizon of 20 years for specific ages. Both models show and predict the decrease in mortality in Ecuador of both genders, a decrease that is more pronounced, in general, for women at certain ages. In determining the uncertainty of the models, the bootstrap technique was used to define the confidence intervals of the adjusted model. The GAPC and ARIMA models were also compared; the former improve the mortality forecasting.

Uncertainty in Mortality Forecasting: An Extension to the Classical Lee-Carter Approach

2009 ◽  
Vol 39 (1) ◽  
pp. 137-164 ◽  
Author(s):  
Johnny Siu-Hang Li ◽  
Mary R. Hardy ◽  
Ken Seng Tan
Keyword(s):  
Individual Differences ◽  
Selection Criteria ◽  
Financial Risk ◽  
Goodness Of Fit ◽  
Mortality Data ◽  
Model Structure ◽  
Death Rates ◽  
Mortality Forecasting ◽  
Mortality Improvement ◽  
Carter Model

AbstractTraditionally, actuaries have modeled mortality improvement using deterministic reduction factors, with little consideration of the associated uncertainty. As mortality improvement has become an increasingly significant source of financial risk, it has become important to measure the uncertainty in the forecasts. Probabilistic confidence intervals provided by the widely accepted Lee-Carter model are known to be excessively narrow, due primarily to the rigid structure of the model. In this paper, we relax the model structure by considering individual differences (heterogeneity) in each age-period cell. The proposed extension not only provides a better goodness-of-fit based on standard model selection criteria, but also ensures more conservative interval forecasts of central death rates and hence can better reflect the uncertainty entailed. We illustrate the results using US and Canadian mortality data.

scholarly journals Evaluation of the Kou-Modified Lee-Carter Model in Mortality Forecasting: Evidence from French Male Mortality Data

Risks ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 123 ◽  
Author(s):  
Marie Angèle Cathleen Alijean ◽  
Jason Narsoo
Keyword(s):  
Mortality Data ◽  
Male Mortality ◽  
Forecasting Accuracy ◽  
Mortality Forecasting ◽  
Mortality Models ◽  
Double Exponential ◽  
Jump Diffusion Model ◽  
Mortality Forecasts ◽  
Death Data ◽  
Carter Model

Mortality forecasting has always been a target of study by academics and practitioners. Since the introduction and rising significance of securitization of risk in mortality and longevity, more in-depth studies regarding mortality have been carried out to enable the fair pricing of such derivatives. In this article, a comparative analysis is performed on the mortality forecasting accuracy of four mortality models. The methodology employs the Age-Period-Cohort model, the Cairns-Blake-Dowd model, the classical Lee-Carter model and the Kou-Modified Lee-Carter model. The Kou-Modified Lee-Carter model combines the classical Lee-Carter with the Double Exponential Jump Diffusion model. This paper is the first study to employ the Kou model to forecast French mortality data. The dataset comprises death data of French males from age 0 to age 90, available for the years 1900–2015. The paper differentiates between two periods: the 1900–1960 period where extreme mortality events occurred for French males and the 1961–2015 period where no significant jump is observed. The Kou-modified Lee-Carter model turns out to give the best mortality forecasts based on the RMSE, MAE, MPE and MAPE metrics for the period 1900–1960 during which the two World Wars occurred. This confirms that the consideration of jumps and leptokurtic features conveys important information for mortality forecasting.

scholarly journals Do Different Models Induce Changes in Mortality Indicators? That Is a Key Question for Extending the Lee-Carter Model

2021 ◽  
Vol 18 (4) ◽  
pp. 2204
Author(s):  
Ana Debón ◽  
Steven Haberman ◽  
Francisco Montes ◽  
Edoardo Otranto
Keyword(s):  
Historical Data ◽  
Mortality Rates ◽  
Parametric Model ◽  
Mortality Data ◽  
Block Bootstrap ◽  
Functional Anova ◽  
Model Block ◽  
The Usa ◽  
Data Constraints ◽  
Carter Model

The parametric model introduced by Lee and Carter in 1992 for modeling mortality rates in the USA was a seminal development in forecasting life expectancies and has been widely used since then. Different extensions of this model, using different hypotheses about the data, constraints on the parameters, and appropriate methods have led to improvements in the model’s fit to historical data and the model’s forecasting of the future. This paper’s main objective is to evaluate if differences between models are reflected in different mortality indicators’ forecasts. To this end, nine sets of indicator predictions were generated by crossing three models and three block-bootstrap samples with each of size fifty. Later the predicted mortality indicators were compared using functional ANOVA. Models and block bootstrap procedures are applied to Spanish mortality data. Results show model, block-bootstrap, and interaction effects for all mortality indicators. Although it was not our main objective, it is essential to point out that the sample effect should not be present since they must be realizations of the same population, and therefore the procedure should lead to samples that do not influence the results. Regarding significant model effect, it follows that, although the addition of terms improves the adjustment of probabilities and translates into an effect on mortality indicators, the model’s predictions must be checked in terms of their probabilities and the mortality indicators of interest.

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

Risks ◽  
2019 ◽  
Vol 7 (1) ◽  
pp. 22 ◽  
Author(s):  
Han Li ◽  
Colin O’Hare
Keyword(s):  
Predictive Power ◽  
Historical Data ◽  
Mortality Rates ◽  
Point Of View ◽  
Mortality Data ◽  
Male Mortality ◽  
Historical Information ◽  
Mortality Forecasting ◽  
Mortality Models ◽  
Forecasting Performance

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.

Negative binomial version of the Lee–Carter model for mortality forecasting

2007 ◽  
Vol 23 (5) ◽  
pp. 385-401 ◽  
Author(s):  
Antoine Delwarde ◽  
Michel Denuit ◽  
Christian Partrat
Keyword(s):  
Negative Binomial ◽  
Mortality Forecasting ◽  
Carter Model

Fuzzy formulation of the Lee–Carter model for mortality forecasting

2006 ◽  
Vol 39 (3) ◽  
pp. 287-309 ◽  
Author(s):  
Marie-Claire Koissi ◽  
Arnold F. Shapiro
Keyword(s):  
Mortality Forecasting ◽  
Carter Model
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