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Author: Debón Aucejo, Ana María × Subject: Forecasting ×

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Now showing 1 - 4 of 4
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    Modelling and forecasting mortality in Spain
    (Elsevier, 2008-09) Debón Aucejo, Ana María; Montes, F.; Puig, F; Facultad de Administración y Dirección de Empresas; Dpto. de Estadística e Investigación Operativa Aplicadas y Calidad; Centro de Gestión de la Calidad y del Cambio; Ministerio de Educación y Ciencia; Generalitat Valenciana
    [EN] Experience shows that static life tables overestimate death probabilities. As a consequence of this overestimation the premiums for annuities, pensions and life insurance are not what they actually should be, with negative effects for insurance companies or policy-holders. The reason for this overestimation is that static life tables, through being computed for a specific period of time, cannot take into account the decreasing mortality trend over time. Dynamic life tables overcome this problem by incorporating the influence of the calendar when graduating mortality. Recent papers on the topic look for the development of new methods to deal with this dynamism. Most methods used in dynamic tables are parametric, apply traditional mortality laws and then analyse the evolution of estimated parameters with time series techniques. Our contribution consists in extending and applying Lee–Carter methods to Spanish mortality data, exploring residuals and future trends.
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    Breast cancer mortality in Spain: Has it really declined?
    (WB Saunders, 2012-10) Álvaro Meca, A.; Debón Aucejo, Ana María; Gil Prieto, R.; Gil de Miguel, Á.; Facultad de Administración y Dirección de Empresas; Dpto. de Estadística e Investigación Operativa Aplicadas y Calidad; Centro de Gestión de la Calidad y del Cambio; Ministerio de Ciencia e Innovación
    Objectives: In recent years, the incidence of breast cancer has increased in Spain but mortality has decreased, particularly since 1992. Despite the general decrease in mortality, the intensity of this disease differs between age groups. The main objective of this study was to examine mortality due to breast cancer for different age groups in Spain from 1981 to 2007, and to forecast the mortality rate in 2023. Study design: Ecological study. Methods: Trends in mortality due to breast cancer were analysed using the LeeeCarter model, which is the typical analysis for mortality in the general population but is rarely used to analyse specific causes of death. Results: This study found a decreasing trend in mortality due to breast cancer from 1993 to 2007, and it is predicted that this trend will continue. However, mortality rates varied between age groups: a decreasing trend was seen in younger and middle-aged women, whereas mortality rates remained stable in older women. Conclusions: Preventive breast cancer practices should differ by patient age.
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    Do Different Models Induce Changes in Mortality Indicators? That Is a Key Question for Extending the Lee-Carter Model
    (MDPI AG, 2021-02-23) Debón Aucejo, Ana María; Haberman, Steven; Montes Suay, Francisco; Otranto, Edoardo; Facultad de Administración y Dirección de Empresas; Dpto. de Estadística e Investigación Operativa Aplicadas y Calidad; Centro de Gestión de la Calidad y del Cambio; Ministerio de Educación; Universitat Politècnica de València
    [EN] 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.
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    A Comparison of Forecasting Mortality Models Using Resampling Methods
    (MDPI AG, 2020-09) Atance, David; Debón Aucejo, Ana María; Navarro, Eliseo; Facultad de Administración y Dirección de Empresas; Dpto. de Estadística e Investigación Operativa Aplicadas y Calidad; Centro de Gestión de la Calidad y del Cambio; Universidad de Alcalá; Agencia Estatal de Investigación; Ministerio de Economía y Competitividad
    [EN] The accuracy of the predictions of age-specific probabilities of death is an essential objective for the insurance industry since it dramatically affects the proper valuation of their products. Currently, it is crucial to be able to accurately calculate the age-specific probabilities of death over time since insurance companies' profits and the social security of citizens depend on human survival; therefore, forecasting dynamic life tables could have significant economic and social implications. Quantitative tools such as resampling methods are required to assess the current and future states of mortality behavior. The insurance companies that manage these life tables are attempting to establish models for evaluating the risk of insurance products to develop a proactive approach instead of using traditional reactive schemes. The main objective of this paper is to compare three mortality models to predict dynamic life tables. By using the real data of European countries from the Human Mortality Database, this study has identified the best model in terms of the prediction ability for each sex and each European country. A comparison that uses cobweb graphs leads us to the conclusion that the best model is, in general, the Lee-Carter model. Additionally, we propose a procedure that can be applied to a life table database that allows us to choose the most appropriate model for any geographical area.