A Modified Lee-Carter Model for Age-Specific Fertility Rate Forecasting: Modelling Ethnic-Based Fertility Change in Malaysia

Accurate fertility prediction is crucial for long-term policy planning, particularly in health economics and demographics. This article proposes a modified version of the Lee-Carter model, which incorporates the skew-logistic probability density function to accurately capture the unimo dal curves of...

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Bibliographic Details
Published in:MATEMATIKA
Main Authors: Shair, Syazreen N.; Nasir, Muhammad S. A. M.; Fadhil, Nur F. M.; Zaidi, Nur F. Z.; Roslan, Muhammad A.
Format: Article
Language:English
Published: PENERBIT UTM PRESS 2024
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Online Access:https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-recordWOS:001371718800002
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Summary:Accurate fertility prediction is crucial for long-term policy planning, particularly in health economics and demographics. This article proposes a modified version of the Lee-Carter model, which incorporates the skew-logistic probability density function to accurately capture the unimo dal curves of age-specific fertility rates (ASFRs) in Malaysia. The model was fitted into Malaysian age-specific fertility data between 1958 and 2005, categorised by three major ethnic groups: Malay, Chinese and Indian. The forecast performances of both the modified and original Lee-Carter models were evaluated by estimating out-of-sample errors between 2006 and 2021. The model that demonstrated the highest accuracy was used to forecast ASFRs between 2022 and 2041. The analyses revealed a notable decline in overall fertility trends over the years, with particularly pronounced decreases observed among the Chinese and Indian populations. Furthermore, there has been a shift towards delayed childbirth, with the highest number of births occurring at older ages in recent years. The results indicate that the modified Lee-Carter model outperforms the original version for the Chinese and Indian populations, suggesting its ability to capture recent significant changes in fertility patterns and enhancing predictive accuracy.
ISSN:0127-8274