The first paper to be presented 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?
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. 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.
A relatively recent literature has developed on modelling mortality in multiple populations together. These studies are driven by two distinct motivations. The first motivation is to improve the accuracy of forecasts in smaller populations. Using robust information from mortality trends for large populations may help to give more accurate or more reasonable forecasts in smaller populations for the purposes of public financing decisions or health care planning (Li and Lee, 2005; Jarner and Kryger, 2009).
A second motivation comes from the actuarial literature. In longevity risk contracts, a fixed amount is paid based on expected mortality rates in return for a payment based on actual Realised mortality rates (a ‘q-forward’). The reference population for the purpose of pricing these products is based on the mortality experience of a given national population. However, the mortality experience of the population used in pricing the hedging instrument may differ from the population of the pension plan (Li and Hardy, 2011; Dowd et al., 2011). These papers have generated a number of statistical models linking mortality in different populations. The purpose of the second paper is to suggest a reason why these relationships exist based on an economic literature on technology and knowledge diffusion. Insights from this paper may help to improve these models and also help to deepen understanding of the processes driving international longevity trends.
The presentation for this event will be available shortly.
Further details are available here