A Bibliometric Analysis of Research on Stochastic Mortality Modelling and Forecasting
Mortality improvements and life expectancies have been increasing in recent decades, leading to growing interest in understanding mortality risk and longevity risk. Studies of mortality forecasting are of interest among actuaries and demographers because mortality forecasting can quantify mortality...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-10-01
|
| Series: | Risks |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-9091/10/10/191 |
| _version_ | 1797470014153949184 |
|---|---|
| author | Norkhairunnisa Redzwan Rozita Ramli |
| author_facet | Norkhairunnisa Redzwan Rozita Ramli |
| author_sort | Norkhairunnisa Redzwan |
| collection | DOAJ |
| description | Mortality improvements and life expectancies have been increasing in recent decades, leading to growing interest in understanding mortality risk and longevity risk. Studies of mortality forecasting are of interest among actuaries and demographers because mortality forecasting can quantify mortality and longevity risks. There is an abundance of literature on the topic of modelling and forecasting mortality, which often leads to confusion in determining a particular model to be adopted as a reliable tool. In this study, we conducted a bibliometric analysis with a focus on citation and co-citation analyses and co-occurrences of keywords to determine the most widely used stochastic mortality model. We found that the Lee–Carter model has remained one of the most relevant mortality models since its development in the 1990s. Furthermore, we also aimed to identify emerging topics and trends relating to mortality modelling and forecasting based on an analysis of authors’ keywords. This study contributes to the literature by providing a comprehensive overview and evolution of publications in stochastic mortality modelling and forecasting. Researchers can benefit from the present work in determining and exploring emerging trends and topics for future studies. |
| first_indexed | 2024-03-09T19:31:44Z |
| format | Article |
| id | doaj.art-7a009213e145416a9be5bba8465c0c8c |
| institution | Directory Open Access Journal |
| issn | 2227-9091 |
| language | English |
| last_indexed | 2024-03-09T19:31:44Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Risks |
| spelling | doaj.art-7a009213e145416a9be5bba8465c0c8c2023-11-24T02:23:10ZengMDPI AGRisks2227-90912022-10-01101019110.3390/risks10100191A Bibliometric Analysis of Research on Stochastic Mortality Modelling and ForecastingNorkhairunnisa Redzwan0Rozita Ramli1Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, Shah Alam 40450, MalaysiaDepartment of Mathematical Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600, MalaysiaMortality improvements and life expectancies have been increasing in recent decades, leading to growing interest in understanding mortality risk and longevity risk. Studies of mortality forecasting are of interest among actuaries and demographers because mortality forecasting can quantify mortality and longevity risks. There is an abundance of literature on the topic of modelling and forecasting mortality, which often leads to confusion in determining a particular model to be adopted as a reliable tool. In this study, we conducted a bibliometric analysis with a focus on citation and co-citation analyses and co-occurrences of keywords to determine the most widely used stochastic mortality model. We found that the Lee–Carter model has remained one of the most relevant mortality models since its development in the 1990s. Furthermore, we also aimed to identify emerging topics and trends relating to mortality modelling and forecasting based on an analysis of authors’ keywords. This study contributes to the literature by providing a comprehensive overview and evolution of publications in stochastic mortality modelling and forecasting. Researchers can benefit from the present work in determining and exploring emerging trends and topics for future studies.https://www.mdpi.com/2227-9091/10/10/191bibliometric analysismortalityforecastingmodelVOSviewer |
| spellingShingle | Norkhairunnisa Redzwan Rozita Ramli A Bibliometric Analysis of Research on Stochastic Mortality Modelling and Forecasting Risks bibliometric analysis mortality forecasting model VOSviewer |
| title | A Bibliometric Analysis of Research on Stochastic Mortality Modelling and Forecasting |
| title_full | A Bibliometric Analysis of Research on Stochastic Mortality Modelling and Forecasting |
| title_fullStr | A Bibliometric Analysis of Research on Stochastic Mortality Modelling and Forecasting |
| title_full_unstemmed | A Bibliometric Analysis of Research on Stochastic Mortality Modelling and Forecasting |
| title_short | A Bibliometric Analysis of Research on Stochastic Mortality Modelling and Forecasting |
| title_sort | bibliometric analysis of research on stochastic mortality modelling and forecasting |
| topic | bibliometric analysis mortality forecasting model VOSviewer |
| url | https://www.mdpi.com/2227-9091/10/10/191 |
| work_keys_str_mv | AT norkhairunnisaredzwan abibliometricanalysisofresearchonstochasticmortalitymodellingandforecasting AT rozitaramli abibliometricanalysisofresearchonstochasticmortalitymodellingandforecasting AT norkhairunnisaredzwan bibliometricanalysisofresearchonstochasticmortalitymodellingandforecasting AT rozitaramli bibliometricanalysisofresearchonstochasticmortalitymodellingandforecasting |