A dynamic parameterization modeling for the age-period-cohort mortality

被引：20

作者：

Hatzopoulos, P. ^{[1
]}

Haberman, S. ^{[2
]}

机构：

[1] Univ Aegean, Dept Stat & Actuarial Financial Math, Samos 83200, Greece

[2] City Univ London, Sir John Cass Business Sch, Fac Actuarial Sci & Insurance, London EC1Y 8TZ, England

来源：

INSURANCE MATHEMATICS & ECONOMICS | 2011年 / 49卷 / 02期

关键词：

Cohort; Mortality forecasting; Generalized linear models; Sparse principal component analysis; Factor analysis; Dynamic linear regression; Bootstrap confidence intervals; FORECASTING MORTALITY; REDUCTION FACTORS; CARTER; PROJECTIONS; RATES;

D O I：

10.1016/j.insmatheco.2011.02.007

中图分类号：

F [经济];

学科分类号：

02 ;

摘要：

An 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). (C) 2011 Elsevier B.V. All rights reserved.

引用

页码：155 / 174

页数：20

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