Modeling and Forecasting Mortality Using the  Lee-Carter Model for Indian Population Based on Decade-wise Data

Chavhan, Rajendra N.; Shinde, Ramkrishna L.

doi:10.4038/sljastats.v17i1.7845

Cited by 11 publications

(8 citation statements)

References 10 publications

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“…This results, together with the results of studies done on the Peruvian population and Indian population by Cerda-Hernández and Sikov (2018) and Chavhan and Shinde (2016) respectively, confirm the importance of finding an appropriate model for modelling the motility index before forecasting.…”

Section: Arimasupporting

confidence: 78%

“…On the contrary, studies done in Malaysia did not consider other ARIMA models. For this reason, in this study we adopt the methodology suggested by Chavhan and Shinde, (2016), where 9 different possible choices of ARIMA models are fitted to the mortality index and compared to acquire the best fitted model using two most commonly used model selection criterions, Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) (Burnham and Anderson, 2004).…”

Section: Arimamentioning

confidence: 99%

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A new approach to forecast Malaysian mortality rates

al.¹

2019

Int. j. adv. appl. sci.

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In this paper a new approach to project the time varying index for the Lee-Carter (LC) model by using a machine learning technique known as Neural Network (NN) is proposed for forecasting Malaysian male and female mortality rates. To evaluate the forecasting performance of the proposed model, the conventional LC model which uses ARIMA to forecast is used as a benchmark. However unlike previous studies were done in Malaysian, we employed 9 different ARIMA models and evaluated the AIC and BIC to obtain the best fit model to forecast. The forecasting performance of the two methodologies were then compared using 3 performance indicators Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). In this study, findings showed that the proposed NN model outperformed the conventional ARIMA model for forecasting both Malaysian male and female mortality rates.

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Section: Arimasupporting

confidence: 78%

Section: Arimamentioning

confidence: 99%

A new approach to forecast Malaysian mortality rates

al.¹

2019

Int. j. adv. appl. sci.

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“…These differences may also be a result of risk factors, societal issues and behavioral choices which influence individuals' survival (Alicandro et al, 2018). Chavhan and Shinde (2016) pointed out that forecasting and modeling mortality have been a major study area for the demographers and actuaries. According to these authors, a lot of states or nations have witnessed significant improvements in death rates during the last century, resulting in improved life expectancy.…”

Section: Introductionmentioning

confidence: 99%

Modeling Mortality by Causes of Death in South Africa Using Log-linear Analysis

Chireshe

2021

Preprint

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This study aimed at identifying underlying patterns in mortality due to causes of death in South Africa using mortality statistics from 2005 to 2015 obtained from Statistics South Africa. Log-linear analysis was used in this study on mortality by causes of death dataset having three variables, cause of death (C), province (P) and year (Y). Log-linear analysis was preferred because of its capability to tease out relationships among variables. Results revealed that there are variations in mortality due to causes of death. Mortality was found to differ widely across the country, among provinces. It is recommended that prevention and management policies for HIV and TB be intensified since they still remain South Africa's major causes of death. A replication of the study could be done in another developing country using latest data to see if it will yield the same results. A multi-population mortality modeling could also be carried out using the same approach.

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“…Apart from the above, the LC model had been introduced in other country in predicting the mortality rate. The LC model had been used in predicting the mortality rate by Hernandez and Sikov [8] in Peruvian, Zili et al [9] in Indonesia, Basnayake and Nawarathna [10] in Norway, Chavhan and Shinde [11] in India, Calma and Revadulla [12] in the Philippines, Taruvinga et al [13] in Zimbabwe, Ngataman et al [14], and Kamaruddin and Ismail [15] in Malaysia.…”

Section: Introductionmentioning

confidence: 99%

Forecasting mortality rates using hybrid Lee–Carter model, artificial neural network and random forest

Hong

Yap

Selvachandran

et al. 2020

Complex Intell. Syst.

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Inaccurate prediction would cause the insurance company encounter catastrophic losses and may lead to overpriced premiums where low-earning consumers cannot afford to insure themselves. The ability to forecast mortality rates accurately can allow the insurance company to take preventive measures to introduce new policies with reasonable prices. In this paper, several Lee-Carter (LC) based models are used to forecast the mortality rates in a case study of the Malaysian population. The LC-ARIMA model and also a combination of the LC model with two machine learning (ML) methods, namely the random forest (RF) and artificial neural network (ANN) methods are utilized on the prediction of mortality rates for males and females in Malaysia, whereby the LC-Random Forest (LC-RF) hybrid model is a new model that is introduced in this paper. Seventeen years of mortality data in Malaysia are selected as the dataset for this research. To analyze how the forecasting models perform for other countries, we have determined the model that has the best fit and produced the best forecasted mortality rates for all the other countries that are studied. This research has showed that LC-ANN and LC-ARIMA are the best model in predicting the mortality rates of males and females in Malaysia, respectively. This study has also found that the LC-ARIMA model is the best performing model in forecasting the mortality rates in countries that have longer life expectancy and a good healthcare system such as

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Modeling and Forecasting Mortality Using the Lee-Carter Model for Indian Population Based on Decade-wise Data

Abstract: The stochastic mortality model given by Lee and Carter (1992)

Cited by 11 publications

References 10 publications

A new approach to forecast Malaysian mortality rates

A new approach to forecast Malaysian mortality rates

Modeling Mortality by Causes of Death in South Africa Using Log-linear Analysis

Forecasting mortality rates using hybrid Lee–Carter model, artificial neural network and random forest

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