A dynamic parameterization modeling for the age-period-cohort mortality
AbstractAn extended version of Hatzopoulos and Haberman (2009) dynamic parametric model is proposed for analyzing mortality structures, incorporating the cohort effect. A one-factor parameterized exponential polynomial in age effects within the generalized linear models (GLM) framework is used. Sparse principal component analysis (SPCA) is then applied to time-dependent GLM parameter estimates and provides (marginal) estimates for a two-factor principal component (PC) approach structure. Modeling the two-factor residuals in the same way, in age-cohort effects, provides estimates for the (conditional) three-factor age-period-cohort model. The age-time and cohort related components are extrapolated using dynamic linear regression (DLR) models. An application is presented for England & Wales males (1841-2006).
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Bibliographic InfoArticle provided by Elsevier in its journal Insurance: Mathematics and Economics.
Volume (Year): 49 (2011)
Issue (Month): 2 (September)
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Web page: http://www.elsevier.com/locate/inca/505554
Cohort Mortality forecasting Generalized linear models Sparse principal component analysis Factor analysis Dynamic linear regression Bootstrap confidence intervals;
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