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Abstract

A new method is proposed of constructing mortality forecasts. This parameterized approach utilizes Generalized Linear Models (GLMs), based on heteroscedastic Poisson (non-additive) error structures, and using an orthonormal polynomial design matrix. Principal Component (PC) analysis is then applied to the cross-sectional fitted parameters. The produced model can be viewed either as a one-factor parameterized model where the time series are the fitted parameters, or as a principal component model, namely a log-bilinear hierarchical statistical association model of Goodman [Goodman, L.A., 1991. Measures, models, and graphical displays in the analysis of cross-classified data. J. Amer. Statist. Assoc. 86(416), 1085–1111] or equivalently as a generalized Lee–Carter model with p interaction terms. Mortality forecasts are obtained by applying dynamic linear regression models to the PCs. Two applications are presented: Sweden (1751–2006) and Greece (1957–2006).

Publication Type:	Article
Additional Information:	NOTICE: this is the author’s version of a work that was accepted for publication in <Journal title>. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Insurance: Mathematics and Economics, Volume 44, Issue 1, February 2009, Pages 103–123, http://dx.doi.org/10.1016/j.insmatheco.2008.10.008
Publisher Keywords:	Mortality forecasting, Generalized linear models, Principal component analysis, Dynamic linear regression, Bootstrap confidence intervals
Subjects:	H Social Sciences > HG Finance
Departments:	Bayes Business School > Finance
Related URLs:	Author
SWORD Depositor:	Symplectic Administrator

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