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Detecting Common Longevity Trends by a Multiple Population Approach

D'Amato, V., Haberman, S., Piscopo, G. , Russolillo, M. & Trapani, L. (2014). Detecting Common Longevity Trends by a Multiple Population Approach. North American Actuarial Journal, 18(1), pp. 139-149. doi: 10.1080/10920277.2013.875884


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.

Publication Type: Article
Additional Information: This is an Accepted Manuscript of an article published by Taylor & Francis in North American Actuarial Journal on 25 Feb 2014, available online:
Publisher Keywords: Serial and Cross-sectional Correlation, Factor Models, Vector AutoRegression, Sieve Bootstrap, Lee Carter model
Subjects: H Social Sciences > HG Finance
Departments: Bayes Business School > Actuarial Science & Insurance
SWORD Depositor:
[thumbnail of DAmatoHabermanPiscopoRussolilloTrapani29Dec2012.pdf]
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