Lee-Carter method for forecasting mortality for Peruvian Population
Descripción del Articulo
In this article, we have modeled mortality rates of Peruvian female and male populations during the period of 1950-2017 using the two-factor Lee-Carter (LC) model. The stochastic mortality model was introduced by Lee and Carter (1992) and has been used by many authors for fitting a...
| Autores: | , |
|---|---|
| Formato: | artículo |
| Fecha de Publicación: | 2021 |
| Institución: | Universidad Nacional de Trujillo |
| Repositorio: | Revistas - Universidad Nacional de Trujillo |
| Lenguaje: | inglés |
| OAI Identifier: | oai:ojs.revistas.unitru.edu.pe:article/3702 |
| Enlace del recurso: | https://revistas.unitru.edu.pe/index.php/SSMM/article/view/3702 |
| Nivel de acceso: | acceso abierto |
| Materia: | Lee-Carter (LC) model Mortality modeling Forecasting Life expectancy Singular value decomposition (SVD) |
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Lee-Carter method for forecasting mortality for Peruvian PopulationLee-Carter method for forecasting mortality for Peruvian PopulationCerda-Hernández, J.Sikov, A.Lee-Carter (LC) modelMortality modelingForecastingLife expectancySingular value decomposition (SVD)Lee-Carter (LC) modelMortality modelingForecastingLife expectancySingular value decomposition (SVD)In this article, we have modeled mortality rates of Peruvian female and male populations during the period of 1950-2017 using the two-factor Lee-Carter (LC) model. The stochastic mortality model was introduced by Lee and Carter (1992) and has been used by many authors for fitting and forecasting the human mortality rates. The Singular Value Decomposition (SVD) approach is used for estimation of the parameters of the LC model. Utilizing the best fitted auto regressive integrated moving average (ARIMA) model we forecast the values of the time dependent parameter of the LC model for the next thirty years. The forecasted values of life expectancy at different age group with 95% confidence intervals are also reported for the next thirty years. In this research we use the data, obtained from the Peruvian National Institute of Statistics (INEI). In this article, we have modeled mortality rates of Peruvian female and male populations during the period of 1950-2017 using the two-factor Lee-Carter (LC) model. The stochastic mortality model was introduced by Lee and Carter (1992) and has been used by many authors for fitting and forecasting the human mortality rates. The Singular Value Decomposition (SVD) approach is used for estimation of the parameters of the LC model. Utilizing the best fitted auto regressive integrated moving average (ARIMA) model we forecast the values of the time dependent parameter of the LC model for the next thirty years. The forecasted values of life expectancy at different age group with 95% confidence intervals are also reported for the next thirty years. In this research we use the data, obtained from the Peruvian National Institute of Statistics (INEI). National University of Trujillo - Academic Department of Mathematics2021-07-29info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://revistas.unitru.edu.pe/index.php/SSMM/article/view/3702Selecciones Matemáticas; Vol. 8 No. 01 (2021): January-July; 52 - 65Selecciones Matemáticas; Vol. 8 Núm. 01 (2021): Enero-Julio; 52 - 65Selecciones Matemáticas; v. 8 n. 01 (2021): Janeiro-julho; 52 - 652411-1783reponame:Revistas - Universidad Nacional de Trujilloinstname:Universidad Nacional de Trujilloinstacron:UNITRUenghttps://revistas.unitru.edu.pe/index.php/SSMM/article/view/3702/4371Derechos de autor 2021 J. Cerda-Hernández, A. Sikovhttps://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:ojs.revistas.unitru.edu.pe:article/37022021-07-29T15:56:48Z |
| dc.title.none.fl_str_mv |
Lee-Carter method for forecasting mortality for Peruvian Population Lee-Carter method for forecasting mortality for Peruvian Population |
| title |
Lee-Carter method for forecasting mortality for Peruvian Population |
| spellingShingle |
Lee-Carter method for forecasting mortality for Peruvian Population Cerda-Hernández, J. Lee-Carter (LC) model Mortality modeling Forecasting Life expectancy Singular value decomposition (SVD) Lee-Carter (LC) model Mortality modeling Forecasting Life expectancy Singular value decomposition (SVD) |
| title_short |
Lee-Carter method for forecasting mortality for Peruvian Population |
| title_full |
Lee-Carter method for forecasting mortality for Peruvian Population |
| title_fullStr |
Lee-Carter method for forecasting mortality for Peruvian Population |
| title_full_unstemmed |
Lee-Carter method for forecasting mortality for Peruvian Population |
| title_sort |
Lee-Carter method for forecasting mortality for Peruvian Population |
| dc.creator.none.fl_str_mv |
Cerda-Hernández, J. Sikov, A. |
| author |
Cerda-Hernández, J. |
| author_facet |
Cerda-Hernández, J. Sikov, A. |
| author_role |
author |
| author2 |
Sikov, A. |
| author2_role |
author |
| dc.subject.none.fl_str_mv |
Lee-Carter (LC) model Mortality modeling Forecasting Life expectancy Singular value decomposition (SVD) Lee-Carter (LC) model Mortality modeling Forecasting Life expectancy Singular value decomposition (SVD) |
| topic |
Lee-Carter (LC) model Mortality modeling Forecasting Life expectancy Singular value decomposition (SVD) Lee-Carter (LC) model Mortality modeling Forecasting Life expectancy Singular value decomposition (SVD) |
| description |
In this article, we have modeled mortality rates of Peruvian female and male populations during the period of 1950-2017 using the two-factor Lee-Carter (LC) model. The stochastic mortality model was introduced by Lee and Carter (1992) and has been used by many authors for fitting and forecasting the human mortality rates. The Singular Value Decomposition (SVD) approach is used for estimation of the parameters of the LC model. Utilizing the best fitted auto regressive integrated moving average (ARIMA) model we forecast the values of the time dependent parameter of the LC model for the next thirty years. The forecasted values of life expectancy at different age group with 95% confidence intervals are also reported for the next thirty years. In this research we use the data, obtained from the Peruvian National Institute of Statistics (INEI). |
| publishDate |
2021 |
| dc.date.none.fl_str_mv |
2021-07-29 |
| dc.type.none.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
| format |
article |
| status_str |
publishedVersion |
| dc.identifier.none.fl_str_mv |
https://revistas.unitru.edu.pe/index.php/SSMM/article/view/3702 |
| url |
https://revistas.unitru.edu.pe/index.php/SSMM/article/view/3702 |
| dc.language.none.fl_str_mv |
eng |
| language |
eng |
| dc.relation.none.fl_str_mv |
https://revistas.unitru.edu.pe/index.php/SSMM/article/view/3702/4371 |
| dc.rights.none.fl_str_mv |
Derechos de autor 2021 J. Cerda-Hernández, A. Sikov https://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess |
| rights_invalid_str_mv |
Derechos de autor 2021 J. Cerda-Hernández, A. Sikov https://creativecommons.org/licenses/by/4.0/ |
| eu_rights_str_mv |
openAccess |
| dc.format.none.fl_str_mv |
application/pdf |
| dc.publisher.none.fl_str_mv |
National University of Trujillo - Academic Department of Mathematics |
| publisher.none.fl_str_mv |
National University of Trujillo - Academic Department of Mathematics |
| dc.source.none.fl_str_mv |
Selecciones Matemáticas; Vol. 8 No. 01 (2021): January-July; 52 - 65 Selecciones Matemáticas; Vol. 8 Núm. 01 (2021): Enero-Julio; 52 - 65 Selecciones Matemáticas; v. 8 n. 01 (2021): Janeiro-julho; 52 - 65 2411-1783 reponame:Revistas - Universidad Nacional de Trujillo instname:Universidad Nacional de Trujillo instacron:UNITRU |
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Universidad Nacional de Trujillo |
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UNITRU |
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UNITRU |
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Revistas - Universidad Nacional de Trujillo |
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Revistas - Universidad Nacional de Trujillo |
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Nota importante:
La información contenida en este registro es de entera responsabilidad de la institución que gestiona el repositorio institucional donde esta contenido este documento o set de datos. El CONCYTEC no se hace responsable por los contenidos (publicaciones y/o datos) accesibles a través del Repositorio Nacional Digital de Ciencia, Tecnología e Innovación de Acceso Abierto (ALICIA).
La información contenida en este registro es de entera responsabilidad de la institución que gestiona el repositorio institucional donde esta contenido este documento o set de datos. El CONCYTEC no se hace responsable por los contenidos (publicaciones y/o datos) accesibles a través del Repositorio Nacional Digital de Ciencia, Tecnología e Innovación de Acceso Abierto (ALICIA).