Hostname: page-component-78c5997874-xbtfd Total loading time: 0 Render date: 2024-11-10T13:27:50.481Z Has data issue: false hasContentIssue false
  • English
  • Français

Mortality forecasting using a modified Continuous Mortality Investigation Mortality Projections Model for China I: methodology and country-level results

Published online by Cambridge University Press:  20 September 2016

Fei Huang  and
Bridget Browne
Fei Huang*
Affiliation:
Research School of Finance, Actuarial Studies and Statistics, College of Business and Economics, Australian National University, Canberra, ACT 2601, Australia
Bridget Browne
Affiliation:
Research School of Finance, Actuarial Studies and Statistics, College of Business and Economics, Australian National University, Canberra, ACT 2601, Australia
*
*Correspondence to: Fei Huang, Research School of Finance, Actuarial Studies and Applied Statistics, College of Business and Economics, Australian National University, Canberra, ACT 2601, Australia. Tel: +(61) 2 612 57390. Fax: +(61) 2 612 50087. E-mail: fei.huang@anu.edu.au
Get access Rights & Permissions [Opens in a new window]

Abstract

In this paper, we project future mortality rates for actuarial use with Chinese data using a modified Continuous Mortality Investigation (CMI) Mortality Projections Model. The model adopts a convergence structure from “initial” to “long-term” rates of mortality improvement as the process of projection. The initial rates of mortality improvement are derived using two-dimensional P-spline methodology. Given the short history of Chinese data, the long-term rates of mortality improvement are determined by borrowing information from international experience. K-means clustering with dynamic time warping distance is used to classify populations, which is novel in the actuarial mortality research field. The original CMI approach is deterministic, however, in this paper we make it stochastic using techniques outlined by Koller and described by Browne et al. Comparing our results with a pure extrapolative approach, we find that the CMI Mortality Projections Model is more suitable for long-term projections for China.

Keywords

Mortality forecastingCMI Mortality Projections ModelChinaMethodology
Type
Papers
Information
Annals of Actuarial Science , Volume 11 , Issue 1 , March 2017 , pp. 20 - 45
Copyright
© Institute and Faculty of Actuaries 2016 

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

Booth, H., Maindonald, J. & Smith, L. (2002). Applying Lee-Carter under conditions of variable mortality decline. Population Studies, 56(3), 325–336.Google Scholar
Booth, H. & Tickle, L. (2008). Mortality modelling and forecasting: a review of methods. Annals of Actuarial Science, 3(1–2), 3–43.Google Scholar
Browne, B., Duchassaing, J. & Suter, F. (2009). Longevity: a “simple” stochastic modelling of mortality. British Actuarial Journal, 15(S1), 249–265.Google Scholar
Cairns, A.J. (2013). Modelling and management of longevity risk, technical report, Heriot-Watt University, Edinburgh, UK.Google Scholar
Cairns, A.J., Blake, D., Dowd, K., Coughlan, G.D. & Khalaf-Allah, M. (2011). Bayesian stochastic mortality modelling for two populations. ASTIN Bulletin, 41, 29–59.Google Scholar
Cairns, A.J.G., Blake, D., Dowd, K., Coughlan, G.D., Epstein, D., Ong, A. & Balevich, I. (2009). A quantitative comparison of stochastic mortality models using data from England and Wales and the United States. North American Actuarial Journal, 13(1), 1–35.Google Scholar
Camarda, C.G. (2012). MortalitySmooth: an R package for smoothing Poisson counts with P-splines. Journal of Statistical Software, 50(1), 1–24.Google Scholar
Continuous Mortality Investigation Committee (CMI) (2009). Continuous Mortality Investigation: a prototype Mortality Projections Model: part two – detailed analysis, Working Paper No. 39, The Institute of Actuaries and the Faculty of Actuaries, London, UK.Google Scholar
Currie, I.D., Durban, M. & Eilers, P.H. (2004). Smoothing and forecasting mortality rates. Statistical Modelling, 4(4), 279–298.Google Scholar
Giorgino, T. (2009). Computing and visualizing dynamic time warping alignments in R: the DTW package. Journal of Statistical Software , 31, 1–24.Google Scholar
Human Mortality Database (n.d.). University of California, Berkeley (USA), and Max Planck Institute for Demographic Research (Germany). Available online at the address www.mortality.org or www.humanmortality.de [accessed 5-Jan-2014].Google Scholar
Hyndman, R.J., Booth, H. & Yasmeen, F. (2013). Coherent mortality forecasting: the product ratio method with functional time series models. Demography, 50(1), 261–283.Google Scholar
Hyndman, R.J. & Ullah, M.S. (2007). Robust forecasting of mortality and fertility rates: a functional data approach. Computational Statistics & Data Analysis, 51(10), 4942–4956.Google Scholar
Keogh, E.J. & Pazzani, M.J. (2001). Derivative dynamic time warping. In First SIAM International Conference on Data Mining (SDM’ 2001), Chicago, IL, USA.Google Scholar
Koller, M. (2011). Life Insurance Risk Management Essentials. Springer, Berlin, Heidelberg.Google Scholar
Lee, R.D. & Carter, L.R. (1992). Modeling and forecasting U. S. mortality. Journal of the American Statistical Association, 87(419), 659–671.Google Scholar
Li, J.S.-H. & Hardy, M.R. (2011). Measuring basis risk in longevity hedges. North American Actuarial Journal, 15(2), 177–200.Google Scholar
Li, N. & Lee, R. (2005). Coherent mortality forecasts for a group of populations: an extension of the Lee-Carter method. Demography, 42(3), 575–594.Google Scholar
Li, N., Lee, R. & Tuljapurkar, S. (2004). Using the Lee-Carter method to forecast mortality for populations with limited data. International Statistical Review, 72(1), 19–36.Google Scholar
Lu, F. & Yin, S. (2005). An application of Lee-Carter method to forecast Chinese mortality (in Chinese). Journal of Insurance Professional College, 6, 9–11.Google Scholar
MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Volume 1: Statistics, University of California Press, Berkeley, CA, pp. 281–297.Google Scholar
Müller, M. (2007). Information Retrieval for Music and Motion. Springer, Berlin, Heidelberg.Google Scholar
Office of the Chief Actuary (2014). Mortality projections for social security programs in Canada, technical report, Office of the Chief Actuary, Canada.Google Scholar
Renshaw, A. & Haberman, S. (2006). A cohort-based extension to the Lee-Carter model for mortality reduction factors. Insurance: Mathematics and Economics, 38(3), 556–570.Google Scholar
Richards, S., Kirkby, J. & Currie, I. (2006). The importance of year of birth in two-dimensional mortality data. British Actuarial Journal, 12, 5–61.Google Scholar
The Society of Actuaries (2013). Literature review and assessment of mortality improvement rates in the U.S. population: past experience and future long-term trends, technical report, The Society of Actuaries, Schaumburg, IL, USA.Google Scholar
The Society of Actuaries (2014). Exposure draft: Mortality Improvement Scale MP-2014, technical report, The Society of Actuaries, Schaumburg, IL, USA.Google Scholar
Song, S. (2009). Does famine have a long-term effect on cohort mortality? Evidence from the 1959–1961 great leap forward famine in China. Journal of Biosocial Science, 41(4), 469–491.Google Scholar
Tang, C., Browne, B. & Bruhn, A. (2014). Projecting Australian mortality using the CMI Mortality Projections Model. Presented at the Actuaries Institute 2014 Financial Services Forum, 5–6 May 2014, Sydney.Google Scholar
Wang, X. & Huang, S. (2011). Comparison and selection of stochastic mortality models in China (in Chinese). Population and Economics, 1, 82–86.Google Scholar
Zhao, B.B. (2012). A modified Lee-Carter model for analysing short-base-period data. Population Studies, 66(1), 39–52.Google Scholar
Zhao, B.B., Liang, X., Zhao, W. & Hou, D. (2013). Modeling of group-specific mortality in China using a modified Lee-Carter model. Scandinavian Actuarial Journal, 2013(5), 383–402.Google Scholar
Zhao, Z. & Guo, F. (2007). Transition and Challenge: China’s Population at the Beginning of the 21st Century. Oxford University Press, Oxford.Google Scholar