Abstract
Borrowing information from populations with similar structural mortality patterns and trajectories has been well recognized as an useful strategy to the mortality forecasting of a target population. This article presents a flexible framework for the selection of populations from a given candidate pool to assist a target population in mortality forecasting. The defining feature of the framework is the deletion-substitution-addition (DSA) algorithm, which is entirely data driven and versatile to work with any multiple-population model for mortality prediction. In numerical studies, the framework with an extended augmented common factor model is applied to the Human Mortality Database, and the superiority of the proposed framework is evident in mortality forecasting performance.
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