City Research Online

Abstract

A new dynamic parametric model is proposed for analyzing the cohort survival function. A one-factor parameterized polynomial in age effects, complementary log-log link and multinomial cohort responses are utilized, within the generalized linear models (GLM) framework. Sparse Principal component analysis (SPCA) is then applied to cohort dependent parameter estimates and provides (marginal) estimates for a two-factor structure. Modeling the two-factor residuals in a similar way, in age-time effects, provides estimates for the three-factor age-cohort-period model. An application is presented for Sweden, Norway, England & Wales and Denmark mortality experience.

Publication Type:	Article
Additional Information:	© 2015, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/
Publisher Keywords:	Cohort mortality; Multinomial responses; Generalized linear models; Mortality forecasting; Sparse Principal component analysis; Dynamic Linear Regression
Subjects:	H Social Sciences > HD Industries. Land use. Labor > HD61 Risk Management Q Science > QA Mathematics
Departments:	Bayes Business School > Actuarial Science & Insurance
SWORD Depositor:	Symplectic Administrator

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