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Identifying structural breaks in stochastic mortality models

O’Hare, Colin; Li, Youwei

Authors

Colin O’Hare



Abstract

In recent years, the issue of life expectancy has become of utmost importance to pension providers, insurance companies, and government bodies in the developed world. Significant and consistent improvements in mortality rates and hence life expectancy have led to unprecedented increases in the cost of providing for older ages. This has resulted in an explosion of stochastic mortality models forecasting trends in mortality data to anticipate future life expectancy and hence quantify the costs of providing for future aging populations. Many stochastic models of mortality rates identify linear trends in mortality rates by time, age, and cohort and forecast these trends into the future by using standard statistical methods. These approaches rely on the assumption that structural breaks in the trend do not exist or do not have a significant impact on the mortality forecasts. Recent literature has started to question this assumption. In this paper, we carry out a comprehensive investigation of the presence or of structural breaks in a selection of leading mortality models. We find that structural breaks are present in the majority of cases. In particular, we find that allowing for structural break, where present, improves the forecast result significantly.

Citation

O’Hare, C., & Li, Y. (2015). Identifying structural breaks in stochastic mortality models. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering, 1(2), Article 021001. https://doi.org/10.1115/1.4029740

Journal Article Type Article
Acceptance Date Feb 5, 2015
Online Publication Date Apr 20, 2015
Publication Date 2015-06
Deposit Date Mar 19, 2019
Journal ASCE-ASME J. Risk and Uncert. in Engrg. Sys., Part B: Mech. Engrg.
Print ISSN 2332-9017
Electronic ISSN 2332-9025
Peer Reviewed Peer Reviewed
Volume 1
Issue 2
Article Number 021001
DOI https://doi.org/10.1115/1.4029740
Keywords Clearances (Engineering); Modeling; Errors; Fittings; Time series; Fluctuations (Physics); Insurance; Explosions; Governments
Public URL https://hull-repository.worktribe.com/output/1389673
Publisher URL http://risk.asmedigitalcollection.asme.org/article.aspx?articleid=2118545