A quantitative comparison of stochastic mortality models on Italian population data

Carfora, Maria Francesca; Cutillo, Luisa; Orlando, Albina

doi:10.1016/j.csda.2017.03.012

Cited by 17 publications

(18 citation statements)

References 19 publications

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“…The Lee-Carter model with two terms (LC2) represents a particular case of the generalized Booth et al (2002) model, with an additional bilinear term to modify mortality trends over time. It has been applied to mortality data from European countries such as Spain ( Debón et al 2008 ) and Italy ( Carfora et al 2017 ). The expression of LC2 is: …”

Section: Methodsmentioning

confidence: 99%

Mortality forecasting in Colombia from abridged life tables by sex

Diaz

Debón

Giner‐Bosch

2018

Genus

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BackgroundAn adequate forecasting model of mortality that allows an analysis of different population changes is a topic of interest for countries in demographic transition. Phenomena such as the reduction of mortality, ageing, and the increase in life expectancy are extremely useful in the planning of public policies that seek to promote the economic and social development of countries. To our knowledge, this paper is one of the first to evaluate the performance of mortality forecasting models applied to abridged life tables.ObjectiveSelect a mortality model that best describes and forecasts the characteristics of mortality in Colombia when only abridged life tables are available.Data and methodWe used Colombian abridged life tables for the period 1973–2005 with data from the Latin American Human Mortality Database. Different mortality models to deal with modeling and forecasting probability of death are presented in this study. For the comparison of mortality models, two criteria were analyzed: graphical residuals analysis and the hold-out method to evaluate the predictive performance of the models, applying different goodness of fit measures.ResultsOnly three models did not have convergence problems: Lee-Carter (LC), Lee-Carter with two terms (LC2), and Age-Period-Cohort (APC) models. All models fit better for women, the improvement of LC2 on LC is mostly for central ages for men, and the APC model’s fit is worse than the other two. The analysis of the standardized deviance residuals allows us to deduce that the models that reasonably fit the Colombian mortality data are LC and LC2. The major residuals correspond to children’s ages and later ages for both sexes.ConclusionThe LC and LC2 models present better goodness of fit, identifying the principal characteristics of mortality for Colombia.Mortality forecasting from abridged life tables by sex has clear added value for studying differences between developing countries and convergence/divergence of demographic changes.

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Section: Methodsmentioning

confidence: 99%

Mortality forecasting in Colombia from abridged life tables by sex

Diaz

Debón

Giner‐Bosch

2018

Genus

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“…The confidence intervals for the mean error was tighter for both SLC model and FDM for Pakistan and Thailand. A quantitative comparison study of different stochastic mortality models has also reported the consistency in performance of lee-carter and functional demographic models on Italian mortality data [ 19 ].…”

Section: Discussionmentioning

confidence: 99%

“…Now these methodologies has become widely used to attain a broader interpretation and to capture the main structures of vigorous of mortality intensity [12][13][14][15][16][17]. However, in context of different stochastic mortality models comparisons, many studies confirm that there is no single model among them which clearly dominates the others according to measured evaluation standards [18][19][20]. Lee and Tuljapurkar [8] proposed a new method in case of few observations at uneven intervals, and they applied it to China and South Korea data.…”

Section: Introductionmentioning

confidence: 99%

A multi-country comparison of stochastic models of breast cancer mortality with P-splines smoothing approach

Mubarik

2020

BMC Med Res Methodol

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Background Precise predictions of incidence and mortality rates due to breast cancer (BC) are required for planning of public health programs as well as for clinical services. A number of approaches has been established for prediction of mortality using stochastic models. The performance of these models intensely depends on different patterns shown by mortality data in different countries. Methods The BC mortality data is retrieved from the Global burden of disease (GBD) study 2017 database. This study include BC mortality rates from 1990 to 2017, with ages 20 to 80+ years old women, for different Asian countries. Our study extend the current literature on Asian BC mortality data, on both the number of considered stochastic mortality models and their rigorous evaluation using multivariate Diebold-Marino test and by range of graphical analysis for multiple countries. Results Study findings reveal that stochastic smoothed mortality models based on functional data analysis generally outperform on quadratic structure of BC mortality rates than the other lee-carter models, both in term of goodness of fit and on forecast accuracy. Besides, smoothed lee carter (SLC) model outperform the functional demographic model (FDM) in case of symmetric structure of BC mortality rates, and provides almost comparable results to FDM in within and outside data forecast accuracy for heterogeneous set of BC mortality rates. Conclusion Considering the SLC model in comparison to the other can be obliging to forecast BC mortality and life expectancy at birth, since it provides even better results in some cases. In the current situation, we can assume that there is no single model, which can truly outperform all the others on every population. Therefore, we also suggest generating BC mortality forecasts using multiple models rather than relying upon any single model.

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“…A closely related area of application is within actuarial science and, in particular, life insurance. A first step in understanding mortality patterns is to construct a model describing observed death counts or mortality rates, or "force of mortality", across age groups ("period mortality") or within cohorts For a survey of various extensions of the Lee-Carter model, see Booth & Tickle (2008); Haberman & Renshaw (2011); Carfora et al (2017) and the references therein. Another line of work is the Gaussian Bayesian extension of the Lee-Carter model treated in Pedroza (2006), where models with random drifts are discussed.…”

Section: Introductionmentioning

confidence: 99%

Mortality forecasting using a Lexis-based state-space model

Andersson

Lindholm

2020

Ann. actuar. sci.

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A new method of forecasting mortality is introduced. The method is based on the continuous-time dynamics of the Lexis diagram, which given weak assumptions implies that the death count data are Poisson distributed. The underlying mortality rates are modelled with a hidden Markov model (HMM) which enables a fully likelihood-based inference. Likelihood inference is done by particle filter methods, which avoids approximating assumptions and also suggests natural model validation measures. The proposed model class contains as special cases many previous models with the important difference that the HMM methods make it possible to estimate the model efficiently. Another difference is that the population and latent variable variability can be explicitly modelled and estimated. Numerical examples show that the model performs well and that inefficient estimation methods can severely affect forecasts.

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A quantitative comparison of stochastic mortality models on Italian population data

Cited by 17 publications

References 19 publications

Mortality forecasting in Colombia from abridged life tables by sex

Mortality forecasting in Colombia from abridged life tables by sex

A multi-country comparison of stochastic models of breast cancer mortality with P-splines smoothing approach

Mortality forecasting using a Lexis-based state-space model

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