Centro de Documentacion de Fundación MAPFRE - A Parameterized approach to modeling and forecasting mortality

Notes: Sumario: 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), 10851111] or equivalently as a generalized LeeCarter model with p interaction terms. Mortality forecasts are obtained by applying dynamic linear regression models to the PCs. Two applications are presented: Sweden (17512006) and Greece (19572006).Article ORelated records: En: Insurance : mathematics and economics. - Oxford : Elsevier, 1990- = ISSN 0167-6687. - 27/02/2009 Tomo 44 Número 1 - 2009, p. 103-123Materia / lugar / evento: Mortalidad Estadística matemática Bootstrap Esperanza de vida Cálculo actuarial Otros autores: Haberman, S.
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