Forecasting death rates using exogenous determinants

Declan French, Colin O'Hare

Research output: Contribution to journalArticleResearchpeer-review

15 Citations (Scopus)


Mortality models used for forecasting are predominantly based on the statistical properties of time series and do not generally incorporate an understanding of the forces driving secular trends. This paper addresses three research questions: Can the factors found in stochastic mortality-forecasting models be associated with real-world trends in health-related variables? Does inclusion of health-related factors in models improve forecasts? Do resulting models give better forecasts than existing stochastic mortality models? We consider whether the space spanned by the latent factor structure in mortality data can be adequately described by developments in gross domestic product, health expenditure and lifestyle-related risk factors using statistical techniques developed in macroeconomics and finance. These covariates are then shown to improve forecasts when incorporated into a Bayesian hierarchical model. Results are comparable or better than benchmark stochastic mortality models.
Original languageEnglish
Pages (from-to)640 - 650
Number of pages11
JournalJournal of Forecasting
Issue number8
Publication statusPublished - 2014

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