Multi-population mortality forecasting using tensor decomposition |
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Authors: | Yumo Dong Honglin Yu Steven Haberman |
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Affiliation: | 1. Research School of Finance, Actuarial Studies and Applied Statistics, College of Business and Economics, Australian National University, Canberra, ACT, Australia;2. Cass Business School, University of London, London, UK |
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Abstract: | 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. |
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Keywords: | Multi-population mortality forecasting tensor decomposition CPD Tucker SVD |
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