Detecting common longevity trends by a multiple population approach
Recently the interest in the development of country and longevity risk models has been growing. The investigation of long-run equilibrium relationships could provide valuable information about the factors driving changes in mortality, in particular across ages and across countries. In order to inves...
| Main Authors: | , , , , |
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| Format: | Article |
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Taylor & Francis
2014
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| Online Access: | https://eprints.nottingham.ac.uk/49237/ |
| _version_ | 1848797953029832704 |
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| author | D’Amato, Valeria Haberman, Steven Piscopo, Gabriella Russolillo, Maria Trapani, Lorenzo |
| author_facet | D’Amato, Valeria Haberman, Steven Piscopo, Gabriella Russolillo, Maria Trapani, Lorenzo |
| author_sort | D’Amato, Valeria |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Recently the interest in the development of country and longevity risk models has been growing. The investigation of long-run equilibrium relationships could provide valuable information about the factors driving changes in mortality, in particular across ages and across countries. In order to investigate cross-country common longevity trends, tools to quantify, compare, and model the strength of dependence become essential. On one hand, it is necessary to take into account either the dependence for adjacent age groups or the dependence structure across time in a single population setting—a sort of intradependence structure. On the other hand, the dependence across multiple populations, which we describe as interdependence, can be explored for capturing common long-run relationships between countries. The objective of our work is to produce longevity projections by taking into account the presence of various forms of cross-sectional and temporal dependencies in the error processes of multiple populations, considering mortality data from different countries. The algorithm that we propose combines model-based predictions in the Lee-Carter (LC) framework with a bootstrap procedure for dependent data, and so both the historical parametric structure and the intragroup error correlation structure are preserved. We introduce a model which applies a sieve bootstrap to the residuals of the LC model and is able to reproduce, in the sampling, the dependence structure of the data under consideration. In the current article, the algorithm that we build is applied to a pool of populations by using ideas from panel data; we refer to this new algorithm as the Multiple Lee-Carter Panel Sieve (MLCPS). We are interested in estimating the relationship between populations of similar socioeconomic conditions. The empirical results show that the MLCPS approach works well in the presence of dependence. |
| first_indexed | 2025-11-14T20:12:03Z |
| format | Article |
| id | nottingham-49237 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T20:12:03Z |
| publishDate | 2014 |
| publisher | Taylor & Francis |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-492372020-05-04T16:42:46Z https://eprints.nottingham.ac.uk/49237/ Detecting common longevity trends by a multiple population approach D’Amato, Valeria Haberman, Steven Piscopo, Gabriella Russolillo, Maria Trapani, Lorenzo Recently the interest in the development of country and longevity risk models has been growing. The investigation of long-run equilibrium relationships could provide valuable information about the factors driving changes in mortality, in particular across ages and across countries. In order to investigate cross-country common longevity trends, tools to quantify, compare, and model the strength of dependence become essential. On one hand, it is necessary to take into account either the dependence for adjacent age groups or the dependence structure across time in a single population setting—a sort of intradependence structure. On the other hand, the dependence across multiple populations, which we describe as interdependence, can be explored for capturing common long-run relationships between countries. The objective of our work is to produce longevity projections by taking into account the presence of various forms of cross-sectional and temporal dependencies in the error processes of multiple populations, considering mortality data from different countries. The algorithm that we propose combines model-based predictions in the Lee-Carter (LC) framework with a bootstrap procedure for dependent data, and so both the historical parametric structure and the intragroup error correlation structure are preserved. We introduce a model which applies a sieve bootstrap to the residuals of the LC model and is able to reproduce, in the sampling, the dependence structure of the data under consideration. In the current article, the algorithm that we build is applied to a pool of populations by using ideas from panel data; we refer to this new algorithm as the Multiple Lee-Carter Panel Sieve (MLCPS). We are interested in estimating the relationship between populations of similar socioeconomic conditions. The empirical results show that the MLCPS approach works well in the presence of dependence. Taylor & Francis 2014-02-25 Article PeerReviewed D’Amato, Valeria, Haberman, Steven, Piscopo, Gabriella, Russolillo, Maria and Trapani, Lorenzo (2014) Detecting common longevity trends by a multiple population approach. North American Actuarial Journal, 18 (1). pp. 139-149. ISSN 1092-0277 http://www.tandfonline.com/doi/abs/10.1080/10920277.2013.875884 doi:10.1080/10920277.2013.875884 doi:10.1080/10920277.2013.875884 |
| spellingShingle | D’Amato, Valeria Haberman, Steven Piscopo, Gabriella Russolillo, Maria Trapani, Lorenzo Detecting common longevity trends by a multiple population approach |
| title | Detecting common longevity trends by a multiple population approach |
| title_full | Detecting common longevity trends by a multiple population approach |
| title_fullStr | Detecting common longevity trends by a multiple population approach |
| title_full_unstemmed | Detecting common longevity trends by a multiple population approach |
| title_short | Detecting common longevity trends by a multiple population approach |
| title_sort | detecting common longevity trends by a multiple population approach |
| url | https://eprints.nottingham.ac.uk/49237/ https://eprints.nottingham.ac.uk/49237/ https://eprints.nottingham.ac.uk/49237/ |