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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: [email protected]
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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 

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