Structural breaks in mortality models and their consequences

O'Hare, C.; Li, Y.

Abstract

In recent years, the issue of life expectancy has become of upmost importance to pension providers, insurance companies and the 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 in order 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 using standard statistical methods. The modeling approaches used failed to capture the effects of any structural change in the trend and, thus, potentially produced incorrect forecasts of future mortality rates. In this paper, we look at a range of leading stochastic models of mortality and test for structural breaks in the trend time series.

Citation

O'Hare, C., & Li, Y. (2014, July). Structural breaks in mortality models and their consequences. Presented at Second International Conference on Vulnerability and Risk Analysis and Management (ICVRAM) and the Sixth International Symposium on Uncertainty, Modeling, and Analysis (ISUMA), Liverpool, UK

• Presentation Conference Type: Conference Paper (published)
• Conference Name: Second International Conference on Vulnerability and Risk Analysis and Management (ICVRAM) and the Sixth International Symposium on Uncertainty, Modeling, and Analysis (ISUMA)
• Start Date: Jul 13, 2014
• End Date: Jul 16, 2014
• Acceptance Date: Jun 1, 2014
• Online Publication Date: Jul 7, 2014
• Publication Date: Jun 27, 2014
• Deposit Date: Mar 19, 2019
• Publisher: American Society of Civil Engineers
• Pages: 1190-1204
• Series Title: CDRM Monographs
• Series Number: 9
• Book Title: Vulnerability, Uncertainty, and Risk: Quantification, Mitigation, and Management
• ISBN: 9780784413609
• DOI: https://doi.org/10.1061/9780784413609.120
• Public URL: https://hull-repository.worktribe.com/output/1390112
• Publisher URL: https://ascelibrary.org/doi/10.1061/9780784413609.120