Mortality forecasting for the Algerian population with considering cohort effect

Flici, Farid (2015): Mortality forecasting for the Algerian population with considering cohort effect. Published in: Proceeding of the IAA-Life Section Colloquia No. 2015, Oslo (Norway) (June 2015)

Abstract

Mortality forecasting became a big challenge not only for demographers but also for actuaries. Different models were proposed for this issue while insuring effciency and simplicity. These models have been based on time and age dimensions. The analysis of mortality reductions schemes by age shows some inequalities related to age. Generally, the difference is well apparent between lower and higher ages. This can't be only tied to time, but also to the year of birth. Considering the cohort effect in morality forecasting has to improve the fitting quality. In the present paper, we propose to forecast the age-specific mortality rates in Algeria with considering cohort effect by comparison a set of models.

• Item Type: MPRA Paper
• Language: English
• Keywords: Mortality forecasting, Cohort, fitting, Algeria, life annuities
• Subjects: G - Financial Economics > G2 - Financial Institutions and Services > G22 - Insurance ; Insurance Companies ; Actuarial Studies; J - Labor and Demographic Economics > J1 - Demographic Economics > J11 - Demographic Trends, Macroeconomic Effects, and Forecasts; J - Labor and Demographic Economics > J1 - Demographic Economics > J14 - Economics of the Elderly ; Economics of the Handicapped ; Non-Labor Market Discrimination

References

Crofts G., 1998. A Karup-King Formula with Unequal Intervals. Actuarial Research Clearing House. Vol. 1: 299-303.

Flici, F. et Hammouda, N. 2014. Analysis of half-century of mortality change in Algeria: 1962-2012. Working Paper. presented in MEEA Conference. 2014.

Flici, F. 2013. Provisionnement des rentes viagères en Algérie entre Approche statique et approches prospective. WP. IAAColloqium, 2013.

Flici, F. 2014. Longevity and life annuities reserving in Algeria: the use of Lee Carter and RenshawHaberman Models. WP. presented in EAAC2014.

Flici, F. 2014-b. Estimation of the missing data in the Algerian mortality surface by using segmented LC model. WP presented in SMTDA2014.

Haberman, S. and Renshaw, A. 2009. On age-period-cohort parametric mortality rate projections.

Lee, R. and Carter, L. 1992. Modelling and forecasting the time series of U.S. mortality

Li, N., Gerland, P., & Gerland, P. 2011. Modifying the Lee-Carter method to project mortality changes up to 2100. In Annual Meeting of the Population Association of America.

Renshaw, A. and Haberman, S. (2006). A cohort-based extension to the Lee-Carter model for mortality reduction factors

COALE, A., & KISKER, E.E. 1990. Defects in data on old age mortality in the Uni- ted States: New procedures for calculating approximatively accurate mortality schedules and life tables at the highest ages.

Currie, I.D. 2006. Smoothing and forecasting mortality rates with P-splines, Talk given at the Institute of Actuaries, June 2006. http://www.macs.hw.ac.uk/~iain/research/talks/Mortality.pdf

Srivastava, R. B. and Srivastava, P. K. 2012. Comparison of Lagrange's and Newton's interpolating polynomials. Journal of Experimental Sciences, 3(1): 01-04.

Beers, H. S. 1944. six-term formulas for routine actuarial interpolation. The record of the American Institute of Actuaries, 33, part (II) (68): 245-260.

Beers, H. S. 1945. Modified-interpolation formula that minimize fourth differences. The record of the American Institute of Actuaries, 34, part. 1, (69): 14-20. (June)

bell, 1997. Comparing and assessing time series methods for forecasting age specific fertility and mortality rates.

King, G. 1908. On the Construction of Mortality Tables from Census Returns and Records of Deaths. Journal of the Institute of Actuaries. 42, 3, 225-294.(JULY).

Sprague T. B. 1880. Explanation ov a New Formula for Interpolation. Journal of the Institute of Actuaries and Assurance Magazine, 22, 4 , 270-285 (JULY).

Wilmoth, J. R. 1993. Computational methods for fitting and extrapolating the Lee-Carter model of mortality change.

Elo, I.T., and Preston, S. H. 1992. effects of Early-Life Conditions on Adult Mortality: A Review. Population Index, 58, 2, 186-212.

Bengtsson, T. and Broström, G. 2009. Do conditions in early life affect old-age mortality directly and indirectly? Evidence from 19th-century rural Sweden. Social science & medicine. 68(9):1583-90. (May). doi: 10.1016/j.socscimed.2009.02.020. Epub 2009 Mar 13.

Richards, S. J., Kirkby, J. G. & Currie, I. D. 2006. The Importance of Year of Birth in Two-Dimensional Mortality Data. British Actuarial Journal, 12 (I), 5-61.

SHROCK, H.S., SIEGEL, J.S. and Associates (1993). `Interpolation: Selected General Methods'. Readings in Population Research Methodology. Vol. 1: Basic Tools. Social Development Center / UN Population Fund, Chicago, pp. 5-48 : 5-72.

Zhenguo Qiu, Zhichang Jiang, Mengzhe Wang, Juanita Hatcher. 2010. Long-Term Projection Methods:Comparison of Age-PeriodCohort Model-Based Approaches. Prepared by Alberta Health Services for the Canadian Partnership Against Cancer.