Mortality forecasting in Colombia from abridged life tables by sex

Diaz, Gisou; Debón, Ana; Giner‐Bosch, Vicent

doi:10.1186/s41118-018-0038-6

Cited by 11 publications

(7 citation statements)

References 36 publications

(47 reference statements)

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“…In men, the parameter takes negative values between the ages of 20 and 40, which means that despite the trend in the reduction of mortality observed at the general level during the study period, the subpopulation of men between the ages of 20 and 40 years presented the inverse phenomenon: their mortality increased, accentuating the hump effect on male mortality. This hump phenomenon has been mainly associated with the homicides due to the armed conflict [13]. In Figure 2, the parameters of the LC2 model are shown.…”

Section: Fitting Lc and Lc2 Modelsmentioning

confidence: 99%

“…In a previous paper [13], different mortality models were fitted, with the Lee-Carter (LC) and Lee-Carter with two terms (LC2) models providing the best fits. In the statistical software R [48], the LC models can be fitted using the gnm package [49].…”

Section: Fitting Lc and Lc2 Modelsmentioning

confidence: 99%

“…There are also successful applications of the LC model and its different versions for the mortality data of Central America and South America. Examples include the works by Andreozzi [8] and Belliard and Willians [9] for Argentina; García-Guerrero [10] for Mexico; Lee and Rofman [11] for Chile; Aguilar [12] for Costa Rica; and Díaz et al [13] for Colombia. These papers show the usefulness of the LC models in analyzing and modeling mortality in developing countries.…”

Section: Introductionmentioning

confidence: 99%

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Multivariate Control Chart and Lee–Carter Models to Study Mortality Changes

2020

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The mortality structure of a population usually reflects the economic and social development of the country. The purpose of this study was to identify moments in time and age intervals at which the observed probability of death is substantially different from the pattern of mortality for a studied period. Therefore, a mortality model was fitted to decompose the historical pattern of mortality. The model residuals were monitored by the T2 multivariate control chart to detect substantial changes in mortality that were not identified by the model. The abridged life tables for Colombia in the period 1973–2005 were used as a case study. The Lee–Carter model collects information regarding violence in Colombia. Therefore, the years identified as out-of-control in the charts are associated with very early or quite advanced ages of death and are inversely related to the violence that did not claim as many victims at those ages. The mortality changes identified in the control charts pertain to changes in the population’s health conditions or new causes of death such as COVID-19 in the coming years. The proposed methodology is generalizable to other countries, especially developing countries.

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Section: Fitting Lc and Lc2 Modelsmentioning

confidence: 99%

Section: Fitting Lc and Lc2 Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multivariate Control Chart and Lee–Carter Models to Study Mortality Changes

2020

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“…This procedure to evaluate mortality models was used in [55] to select the model that best describes and forecasts mortality rates in Colombia. Other authors such as [38,42,43,45,56,57] have employed this technique although some authors do not refer to it as the hold-out method.…”

Section: Hold-out or Out-of-samplementioning

confidence: 99%

A Comparison of Forecasting Mortality Models Using Resampling Methods

2020

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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.

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“…The UDD assumption are often used as solutions to get annuities with more frequent payments. This will help in measuring the value of the annual term EPV using data on Indonesia's Abridged Life Table . In addition, by getting an annuity that is closer to the actual situation, industry and government can minimize the possible risks in future policy making (Diaz et al, 2018).…”

Section: A Introductionmentioning

confidence: 99%

Mortality Projection on Indonesia's Abridged Life Table to Determine the EPV of Term Annuity

Siswono¹,

Azmi²,

Syaifudin³

2021

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The life insurance industries usually use the Life Table for the valuation process, especially in calculating premiums and policy values of a policy. However, the Life Table is rarely updated; and it may even take years before they are updated. This happens because the insurers believe that the information in the Life Table is still related to the current state of a country and for the next several years. In fact, data and information related to mortality rates in a country are constantly changing and always being updated annually. Therefore, as an approach, researchers use the projection of mortality to approach the mortality rate in the future. Thus, future mortality data can be predicted so that better policies can be made by the governments or insurance industries. In this study, the Abridged Life Table of Indonesia is used in the projection of mortality for both sexes (male and female) of the population in Indonesia. The results of mortality projection are then used to calculate the Expected Present Value (EPV) of a term annuity-due under uniform distribution of deaths (UDD) for several values of and ages. The results obtained show that there is a decrease in the value of the mortality rate in the next few years. Therefore, it can be assumed that there is a possibility for longevity risk to occur in the future.

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Mortality forecasting in Colombia from abridged life tables by sex

Cited by 11 publications

References 36 publications

Multivariate Control Chart and Lee–Carter Models to Study Mortality Changes

Multivariate Control Chart and Lee–Carter Models to Study Mortality Changes

A Comparison of Forecasting Mortality Models Using Resampling Methods

Mortality Projection on Indonesia's Abridged Life Table to Determine the EPV of Term Annuity

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