A parameterized approach to modeling and forecasting mortality |
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Authors: | P Hatzopoulos S Haberman |
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Institution: | a Department of Statistics and Actuarial-Financial Mathematics, University of the Aegean, Samos, 83200, Greece b Faculty of Actuarial Science and Insurance, Sir John Cass Business School, City University, 106 Bunhill Row, London EC1Y8TZ, UK |
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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). |
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Keywords: | Mortality forecasting Generalized linear models Principal component analysis Dynamic linear regression Bootstrap confidence intervals |
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