Enhancing Mortality Forecasting through Bivariate Model–Based Ensemble

Abstract

We propose a bivariate model–based ensemble (BMBE) method to borrow information from the mortality data of a given pool of auxiliary populations to enhance the mortality forecasting of a target population. The BMBE method establishes a cascade of bivariate mortality models between the target population and each auxiliary population as the base learners. Then it aggregates prediction results from all of the base learners by means of an averaging strategy. Augmented common factor–type and CBD-type bivariate models are applied as the base learners as illustrative examples in the empirical studies with the Human Mortality Database. Empirical results presented in this article confirm the effectiveness of the proposed BMBE method in enhancing mortality prediction. For completeness, we also conduct a synthetic study to illustrate a particular setting for the superior performance of the BMBE method.

ACKNOWLEDGMENTS

The authors are grateful for the time spent by the editor in handling our article. The authors are also thankful for the valuable comments from three anonymous reviewers that have led to substantial improvement in the contents and presentation of the article.

Additional information

Funding

The authors acknowledge funding support from the Canadian Institute of Actuaries (No. CS000215).