A parameterized approach to modeling and forecasting mortality. (English) Zbl 1156.91394

Summary: 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 L. A. Goodman [Measures, models, and graphical displays in the analysis of cross-classified data. J. Am. Statist. Assoc. 86, No. 416, 1085–1111 (1991)] 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).


91B30 Risk theory, insurance (MSC2010)


Human Mortality
Full Text: DOI Link


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