Modeling and forecasting mortality rates

Daniel Mitchell, Patrick Brockett, Rafael Mendoza-Arriaga, Kumar Muthuraman

Research output: Contribution to journalArticlepeer-review

52 Scopus citations

Abstract

We show that by modeling the time series of mortality rate changes rather than mortality rate levels we can better model human mortality. Leveraging on this, we propose a model that expresses log mortality rate changes as an age group dependent linear transformation of a mortality index. The mortality index is modeled as a Normal Inverse Gaussian. We demonstrate, with an exhaustive set of experiments and data sets spanning 11 countries over 100 years, that the proposed model significantly outperforms existing models. We further investigate the ability of multiple principal components, rather than just the first component, to capture differentiating features of different age groups and find that a two component NIG model for log mortality change best fits existing mortality rate data.

Original languageEnglish (US)
Pages (from-to)275-285
Number of pages11
JournalInsurance: Mathematics and Economics
Volume52
Issue number2
DOIs
StatePublished - Mar 1 2013

Keywords

  • Mortality forecasting
  • Mortality rates
  • Statistics
  • Time series

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