Hostname: page-component-586b7cd67f-tf8b9 Total loading time: 0 Render date: 2024-11-27T02:07:28.987Z Has data issue: false hasContentIssue false

Mortality Projections using Generalized Additive Models with applications to annuity values for the Irish population

Published online by Cambridge University Press:  12 November 2010

Abstract

Generalized Additive Models (GAMs) with age, period and cohort as possible covariates are used to predict future mortality improvements for the Irish population. The GAMs considered are the 1-dimensional age + period and age + cohort models and the 2-dimensional age-period and age-cohort models. In each case thin plate regression splines are used as the smoothing functions. The generalized additive models are compared with the P-Spline (Currie et al., 2004) and Lee-Carter (Lee & Carter, 1992) models included in version 1.0 of the Continuous Mortality Investigation (CMI) library of mortality projections. Using the Root Mean Square Error to assess the accuracy of future predictions, the GAMs outperform the P-Spline and Lee-Carter models over intervals of 25 and 35 years in the age range 60 to 90. The GAMs allow intuitively simple models of mortality to be specified whilst also providing the flexibility to model complex relationships between the covariates. The majority of morality improvements derived from the projections of future Irish mortality yield annuity values at ages 60, 65, 70 and 80 in 2007 in the range of annuity values calculated, assuming a 2 to 4 percent annual compound improvement in mortality rates for both males and females.

Type
Papers
Copyright
Copyright © Institute and Faculty of Actuaries 2010

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Bashir, S.A., Esteve, J. (2001). Projecting cancer incidence and mortality using Bayesian age-period cohort models. Journal of Epidemiology and Biostatistics, 6, 287296.Google ScholarPubMed
Bray, I. (2002). Application of Markov Chain Monte Carlo Methods to Projecting Cancer Incidence and Mortality. Applied Statistics, 51, 151164.Google Scholar
Clements, M.S., Armstrong, B.K., Moolgavkar, S.H. (2005). Lung cancer rate predictions using generalized additive models. Biostatistics, 6, 576589.CrossRefGoogle ScholarPubMed
Cleries, R., Ribes, J., Esteban, L., Martinez, J.M., Borras, J.M. (2006). Time trends of breast cancer mortality in Spain during the period 1977–2001 and Bayesian approach for projections during 2002-2016. Annals of Oncology, 17(12), 17831791.CrossRefGoogle ScholarPubMed
CMI (2002). Continuous Mortality Investigation Working Paper No 1. Institute and Faculty of Actuaries, U.K.Google Scholar
CMI (2006a). Continuous Mortality Investigation Working Paper No 20. Institute and Faculty of Actuaries, U.K.Google Scholar
CMI (2006b). Continuous Mortality Investigation Working Paper No 22. Institute and Faculty of Actuaries, U.K.Google Scholar
CMI (2007a). Continuous Mortality Investigation Working Paper No 25. Institute and Faculty of Actuaries, U.K.Google Scholar
CMI (2007c). Continuous Mortality Investigation User Guide to Version 1.0 of the CMI Library of Mortality Projections. Institute and Faculty of Actuaries, U.K.Google Scholar
Currie, I.D., Durban, M., Eilers, P.H.C. (2004). Smoothing and forecasting mortality rates. Statistical Modeling, 4, 279298.CrossRefGoogle Scholar
Dominici, F., McDermott, A., Zeger, S.L., Samet, J.M. (2002). On the Use of Generalized Additive Models in Time-Series Studies of Air Pollution and Health. American Journal of Epidemiology, 15, 193203.CrossRefGoogle Scholar
Fewster, R.M., Buckland, S.T., Siriwardena, G.M., Baillie, S.R., Wilson, J.D. (2000). Analysis of Population Trends for Farmland Birds using Generalized Additive Models. Ecology, 81(7), 19701984.CrossRefGoogle Scholar
Gallop, A. (2008). Mortality Projections in the United Kingdom. 2008 Living to 100 and Beyond Symposium, Society of Actuaries, US.Google Scholar
Holford, T.R. (1983). The Estimation of Age, Period and Cohort Effects for Vital Rates. Biometrics, 39, 311324.CrossRefGoogle ScholarPubMed
Lee, R.D., Carter, L. (1992). Modeling and forecasting the time series of U.S. mortality. Journal of the American Statistical Association, 87, 659671.Google Scholar
Nelder, J.A., Wedderburn, R.W.N. (1972). Generalized Linear Models. Journal of the Royal Statistical Society, A, 135, 370384.CrossRefGoogle Scholar
R Development Core Team (2009). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL http://www.R-project.org.Google Scholar
Sithole, T., Haberman, S., Verrall, R. (2000). An investigation into parametric models for mortality projections, with applications to immediate annuitants’ and life office pensioners’ data. Insurance: Mathematics and Economics, 27, 285312.Google Scholar
Whelan, S. (2008). Recent Trends in Mortality and Morbidity in Ireland. Journal of the Statistical and Social Inquiry Society of Ireland.Google Scholar
Wong-Fupuy, C., Haberman, S. (2004). Projecting Mortality Trends: Recent Developments in the United Kingdom and the United States. North American Actuarial Journal, 8, 5683.CrossRefGoogle Scholar
Wood, S.N. (2001). mgcv: GAMs and Generalized Ridge Regression for R. R News, 1, 2025.Google Scholar
Wood, S.N. (2003). Thin plate regression splines. Journal of the Royal Statistical Society B, 65(Part 1), 95114.CrossRefGoogle Scholar
Wood, S.N. (2006). Generalized Additive Models, An Introduction with R. Chapman & Hall.CrossRefGoogle Scholar