Five different distributions for the Lee–Carter model of mortality forecasting: A comparison using GAS models

Neves, César; Fernandes, Cristiano; Hoeltgebaum, Henrique

doi:10.1016/j.insmatheco.2017.04.004

Cited by 17 publications

(13 citation statements)

References 19 publications

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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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Section: Hold-out or Out-of-samplementioning

confidence: 99%

A Comparison of Forecasting Mortality Models Using Resampling Methods

2020

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“…There is a suggestion to use new classes of time series models to predict values of κ t e.g. Generalized Autoregressive Score (GAS) or Dynamic Conditional Score (DCS) models [10]. Some scientists try to use the Lee-Carter approach not only to model the mortality rate, but to model changes in the mortality rate, eg by predicting changes ln (m x,t+1 ) − ln (m x,t ) in the central death rate [9].…”

Section: Stochastic Mortality Modelsmentioning

confidence: 99%

Mortality in a heterogeneous population - Lee-Carter's methodology

Jodź¹

2018

Preprint

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The EU Solvency II directive recommends insurance companies to pay more attention to the risk management methods. The sense of risk management is the ability to quantify risk and apply methods that reduce uncertainty. In life insurance, the risk is a consequence of the random variable describing the life expectancy. The article will present a proposal for stochastic mortality modeling based on the Lee and Carter methodology. The maximum likelihood method is often used to estimate parameters in mortality models. This method assumes that the population is homogeneous and the number of deaths has the Poisson distribution. The aim of this article is to change assumptions about the distribution of the number of deaths. The results indicate that the model can get a better match to historical data, when the number of deaths has a negative binomial distribution.

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“…Currie [4] extended the LC model to a generalized linear model (GLM) framework where the LC model and its extensions were fitted following the GLM framework in the Poisson and binomial settings. Neves et al [5] considered five probability models (Poisson, binomial, negative binomial, Gaussian, and beta) based on the generalized autoregressive score (GAS) model to estimate the LC parameters and forecast mortality rates.…”

Section: Introductionmentioning

confidence: 99%

Hybrid of the Lee-Carter Model with Maximum Overlap Discrete Wavelet Transform Filters in Forecasting Mortality Rates

et al. 2021

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This study implements various, maximum overlap, discrete wavelet transform filters to model and forecast the time-dependent mortality index of the Lee-Carter model. The choice of appropriate wavelet filters is essential in effectively capturing the dynamics in a period. This cannot be accomplished by using the ARIMA model alone. In this paper, the ARIMA model is enhanced with the integration of various maximal overlap discrete wavelet transform filters such as the least asymmetric, best-localized, and Coiflet filters. These models are then applied to the mortality data of Australia, England, France, Japan, and USA. The accuracy of the projecting log of death rates of the MODWT-ARIMA model with the aforementioned wavelet filters are assessed using mean absolute error, mean absolute percentage error, and mean absolute scaled error. The MODWT-ARIMA (5,1,0) model with the BL14 filter gives the best fit to the log of death rates data for males, females, and total population, for all five countries studied. Implementing the MODWT leads towards improvement in the performance of the standard framework of the LC model in forecasting mortality rates.

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Five different distributions for the Lee–Carter model of mortality forecasting: A comparison using GAS models

Cited by 17 publications

References 19 publications

A Comparison of Forecasting Mortality Models Using Resampling Methods

A Comparison of Forecasting Mortality Models Using Resampling Methods

Mortality in a heterogeneous population - Lee-Carter's methodology

Hybrid of the Lee-Carter Model with Maximum Overlap Discrete Wavelet Transform Filters in Forecasting Mortality Rates

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