Mortality modelling with arrival of additional year of mortality data: Calibration and forecasting.

BACKGROUND: For commonly used mortality models, the existing estimates change with the recalibration of new data. This issue is also known as the lack of the new-data-invariant property. OBJECTIVE: We adapt the Lee-Carter, age-period-cohort, Renshaw-Haberman, and Li-Lee models to achieve the new-data-invariant property. The resulting fitted or forecast mortality indexes are tractable and comparable when more recent data are modelled. METHODS: Illustrated by mortality rates of the England andWales populations, we explore the tradeoff between goodness of fit and the new-data-invariant property. Using the adapted model and vector autoregressive framework, we explore the interdependencies of subregional mortality dynamics in the United Kingdom. RESULTS: To compare the goodness of fit, we consider the four adapted models and the Cairns-Blake-Dowd model, which are invariant to new data without adaptation. The Renshaw- Haberman model is demonstrated to be the best-performing model. The in-sample and backtesting results show that the proposed adaptation introduces only a small cost of reduced model fitting, which is robust across sensitivity analyses. CONCLUSIONS: The adapted Renshaw-Haberman model is recommended to construct tractable mortality indexes. CONTRIBUTION: From a methodological perspective, we adopt popular models to achieve a desirable newdata-invariant property. Our empirical results suggest that the adapted model can provide reliable forecast of mortality rates for use in demographic research. [ABSTRACT FROM AUTHOR]