A parameterized approach to modeling and forecasting mortality

P. Hatzopoulos and S. Haberman

Insurance: Mathematics and Economics, 2009, vol. 44, issue 1, pages 103-123

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).

Keywords: Mortality; forecasting; Generalized; linear; models; Principal; component; analysis; Dynamic; linear; regression; Bootstrap; confidence; intervals (search for similar items in EconPapers)
Date: 2009

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Persistent link: http://EconPapers.repec.org/RePEc:eee:insuma:v:44:y:2009:i:1:p:103-123

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