Multi-population mortality forecasting using tensor decomposition

Yumo Dong, Fei Huang*, Honglin Yu, Steven Haberman

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    19 Citations (Scopus)

    Abstract

    In this paper, we formulate the multi-population mortality forecasting problem based on 3-way (age, year, and country/gender) decompositions. By applying the canonical polyadic decomposition (CPD) and the different forms of the Tucker decomposition to multi-population mortality data (10 European countries and 2 genders), we find that the out-of-sample forecasting performance is significantly improved both for individual populations and the aggregate population compared with using the single-population mortality model based on rank-1 singular value decomposition (SVD), or the Lee–Carter model. The results also shed lights on the similarity and difference of mortality among different countries. Additionally, we compare the variance-explained method and the out-of-sample validation method for rank (hyper-parameter) selection. Results show that the out-of-sample validation method is preferred for forecasting purposes.

    Original languageEnglish
    Pages (from-to)754-775
    Number of pages22
    JournalScandinavian Actuarial Journal
    Volume2020
    Issue number8
    DOIs
    Publication statusPublished - 13 Sept 2020

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