Hostname: page-component-cd9895bd7-8ctnn Total loading time: 0 Render date: 2024-12-23T09:48:15.205Z Has data issue: false hasContentIssue false

Multiplicative Hazard Models for Studying the Evolution of Mortality

Published online by Cambridge University Press:  10 May 2011

M. Guillen
Affiliation:
Department of Econometrics, University of Barcelona, Diagonal 690, E-08034 Barcelona, Spain., Email: [email protected]
J. P. Nielsen
Affiliation:
Royal&SunAlliance Codan, Gammel Kongevej 60, 1790, Copenhagen V, Denmark., Email: [email protected]
A. M. Perez-Marin
Affiliation:
Department of Econometrics, University of Barcelona, Diagonal 690, E-08034 Barcelona, Spain., Email: [email protected]

Abstract

Almost all over the world, decreasing mortality rates and increasing life expectancy have led to greater interest in estimating and predicting mortality. Here we describe some of the pitfalls which can result from the use of the standardised mortality ratio (SMR) while evaluating the development of mortality over time, in particular when SMRs are applied to insurance portfolios varying dramatically over time. Although an excellent comparative study of a single-figure index for a number of countries was recently done by Macdonald et al. (1998), we advocate care when attempting to extend this type of method to insurance data. Here we promote the use of genuine multiplicative modelling such as in Felipe et al. (2001), who compared the mortality rates in Denmark and Spain. The starting point for our study was the two-dimensional mortality estimator of Nielsen & Linton (1995), which considers mortality as a function of chronological time and age. From the principle of marginal integration (see Nielsen & Linton, 1995, and Linton et al., 2003), estimators of the multiplicative model can be obtained from this two-dimensional estimator. An application of the method is provided for mortality data of the United States of America, England & Wales, France, Italy, Japan and Russia.

Type
Papers
Copyright
Copyright © Institute and Faculty of Actuaries 2006

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

Andersen, P.K., Borgan, O., Gill, R.D. & Keiding, N. (1992). Statistical models based on counting processes. Springer-Verlag, New York.Google Scholar
Benjamin, B. & Soliman, A.S. (1995). Mortality on the move. City University Print Unit.Google Scholar
Brouhns, N., Denuit, M. & Vermunt, J. (2002). A Poisson log-bilinear approach to the construction of projected lifetables. Insurance, Mathematics and Economics, 31, 3, 373–393.Google Scholar
Buus, H. (1960). Investigations of mortality variations. XVI Congres International d'Actuaires. II, 364378.Google Scholar
Cox, P.R. (1976). Demography. Cambridge University Press.CrossRefGoogle Scholar
Cramer, H. & Wold, H. (1935). Mortality variations in Sweden. A study in graduation and forecasting. Skandinavisk Aktuarietidskrift, 161241.Google Scholar
Felipe, A., Guillen, M. & Nielsen, J.P. (2001). Longevity studies based on kernel hazard estimation. Insurance: Mathematics and Economics, 28, 2, 191–204.Google Scholar
Fledelius, P., Guillen, M., Nielsen, J.P. & Petersen, K. (2004). Longevity studies based on kernel hazard estimation. Journal of Actuarial Practice, 11, 101126.Google Scholar
Haberman, S. (1988). Measuring relative experience. Journal of the Institute of Actuaries, 115, 271298.CrossRefGoogle Scholar
Haberman, S. & Renshaw, A.E. (1996). Generalized linear models and actuarial science. The Statistician, 45, 407436.CrossRefGoogle Scholar
Lee, R. & Carter, L. (1992). Modeling and forecasting U.S. mortality. Journal of the American Statistical Association, 87, 14, 659–671.Google Scholar
Linton, O.B., Nielsen, J.P. & van de Geer, S. (2003). Estimating multiplicative and additive hazards by kernel methods. Annals of Statistics, 31, 2, 464–492.CrossRefGoogle Scholar
Macdonald, A.S., Cairns, A.J., Gwilt, P.L. & Miller, K.A. (1998). An international comparison of recent trends in population mortality. British Actuarial Journal, 4, 3141.Google Scholar
Nielsen, J.P. (1998). Marker dependent kernel hazard estimation from local linear estimation. Scandinavian Actuarial Journal, 113124.Google Scholar
Nielsen, J.P. & Linton, O.B. (1995). Kernel estimation in a nonparametric marker dependent hazard model. Annals of Statistics, 23, 17351748.Google Scholar
Nielsen, J.P. & Tanggaard, C. (2001). Kernel estimation in a nonparametric marker dependent hazard model. Scandinavian Journal of Statistics, 28, 4, 675–698.Google Scholar
Nielsen, J.P. & Voldsgaard, P. (1996). Structured nonparametric marker dependent hazard estimation based on marginal integration. An application to health dependent mortality. Proceedings of 27th Astin Conference in Copenhagen, 2, 634641.Google Scholar
Ramlau-Hansen, H. (1983). Smoothing counting process intensities by means of kernel functions. Annals of Statistics, 11, 453466.Google Scholar
Ramlau-Hansen, H., Jespersen, N.C.B., Andersen, P.K., Borch-Johnsen, K. & Deckert, T. (1987). Life insurance for insulin-dependent diabetics. Scandinavian Actuarial Journal, 1936.CrossRefGoogle Scholar
Renshaw, A.E. & Haberman, S. (2000). Modelling for mortality reduction factors. Actuarial research paper No 127. City University, London.Google Scholar
Renshaw, A.E. & Haberman, S. (2003a). Lee-Carter mortality forecasting: a parallel generalized linear modelling approach for England and Wales mortality projections. Applied Statistics, 52, 119137.Google Scholar
Renshaw, A.E. & Haberman, S. (2003b). On the forecasting of mortality reduction factors. Insurance: Mathematics and Economics, 32, 379401.Google Scholar
Wienke, A., Holm, N.V., Christensen, K., Skytthe, A., Vaupel, J.W. & Yashin, A.I. (2003). The heritability of cause-specific mortality: a correlated gamma-frailty model applied to mortality due to respiratory diseases in Danish twins born 1870–1930. Statistics in Medicine, 22, 38733887.Google Scholar