Forecasting Malaysian mortality rates using the Lee-Carter model with fitting period variants

Ibrahim, Nur Shatikah Mohamad; Lazam, Norazliani Md; Shair, Syazreen Niza

doi:10.1088/1742-6596/1988/1/012103

Cited by 2 publications

(2 citation statements)

References 4 publications

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“…The term 'accident hump' refers to a noticeable hump on the mortality curve that occurs most commonly between the ages of 10 and 40, and is more prominent in males than females (Heligman & Pollard, 1980;Haldrup & Rosenskjold, 2019). The 'accident hump' in Figure 2 may have been caused by male mortality rates, which had a more distinct 'accident hump' than female mortality rates (Ibrahim et al, 2021).…”

Section: A Mortality Trend In Malaysiamentioning

confidence: 99%

Mortality Index Simulation for Forecasting Malaysian Mortality Rates

Redzwan,

Ramli,

Sivasundaram

2023

ASMScJ

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Mortality studies are very important in demography and actuarial areas because they assist policymakers and life insurers in managing longevity and mortality risks. In recent decades, many extrapolative mortality models have been developed following the Lee-Carter model. Despite the widely used Lee-Carter model for projecting mortality rates, the literature that has a thorough explanation of it is limited. In this study, we aim to provide a comprehensive explanation of the model with a focus on its fitting and simulation forecasting techniques. We fitted the mortality rates of the Malaysian population for the years 1991 to 2012 using the Lee-Carter model. We then projected the mortality rates for the years 2013 to 2018 using an autoregressive integrated moving average (ARIMA) (0,1,0) model by using a simulation of the mortality index. Findings showed that the Lee-Carter model performs well for this dataset based on the computed standard accuracy measures. The estimated age parameters exhibited a high mortality rate in the age group of 0-4 years, while the estimated time-varying parameter indicated a decreasing trend. This study presents a thorough interpretation of the Lee-Carter model and a detailed simulation of the ARIMA (0,1,0) model and hence provides a comprehensive reference for beginners in mortality studies.

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Section: A Mortality Trend In Malaysiamentioning

confidence: 99%

Mortality Index Simulation for Forecasting Malaysian Mortality Rates

Redzwan,

Ramli,

Sivasundaram

2023

ASMScJ

View full text Add to dashboard Cite

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“…Age specific mortality is expressed as a function of unobserved time specific intensity index and an additional parameter dependent on age. The model has been applied in analyzing mortality rates in different countries such as; Chile (Lee and Rofman [16]), G7 countries (Tuljapurkar et al [17]), Australia ( Booth et al [18], Carter and Prskawetz [19], and Booth and Tickle [20]), Sweden (Lundström and Qvist [21]), Spain (Debón et al [22]), India (Chavhan and Shinde [23]), Malaysia (Ibrahim et al [24]), China & Pakistan (Mubarik et al [25]), Italy (Carfora and Orlando [26]) and Bangladesh (Fazle and Khan [27]).…”

Section: Introductionmentioning

confidence: 99%

Modelling Mortality in Kenya

Njenga,

Kipchirchir

2024

ARJOM

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This research work seeks to analysis the mortality trend experienced in Kenya over the sample period 1950 to 2021 using a multidimensional modeling framework. Life table functions, namely; life expectancy, survival function and age at death distribution are applied to depict mortality characteristics. Life expectancy and survival rate have significantly improved. There has been a shift in mortality status from a high mortality population, to an intermediate stage and mortality risk factors have increased across age. Mortality concentration curve and index within the Lorenz curve and Gini coefficient framework are used to analyze the lifespan inequality. Lifespan inequality is high with negligible improvements over time. Gompertz force of mortality is then estimated, which is statistically significant at 5% level. Deaths at exact age 25 is about 35 per ten thousand, with the rate death rate increasing by 6.09% per year starting from age 25. Under the assumptions of stable population, where the mortality and fertility functions are independent of time, Malthusian parameter is estimated which is less than zero for selected years. Kenya is a shrinking population and death rate decrease with increase in Malthusian parameter. Finally, to model long-term mortality rate forecast, Lee-Carter model is estimated. The model is statistically significant at 5% level explaining 78.4% of the variations. Expected life expectancy at a given age is projected to increase, with life expectancy at birth in 2030 and 2071 being 65.6 and 70.5 years respectively.

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Forecasting Malaysian mortality rates using the Lee-Carter model with fitting period variants

Cited by 2 publications

References 4 publications

Mortality Index Simulation for Forecasting Malaysian Mortality Rates

Mortality Index Simulation for Forecasting Malaysian Mortality Rates

Modelling Mortality in Kenya

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