Tang, ChenShang , Han LinYang, YanrongYang, Yang2026-05-292026-05-290964-1998WOS:001588113700001ORCID:/0000-0002-0948-6073/work/196798510https://hdl.handle.net/1885/733809680We propose a dual-factor model for high-dimensional functional time series (HDFTS) that considers multiple populations. The HDFTS is first decomposed into a collection of functional time series (FTS) in a lower dimension and a group of population-specific basis functions. The system of basis functions describes cross-sectional heterogeneity, while the reduced-dimension FTS retains most of the information common to multiple populations. The low-dimensional FTS is further decomposed into a product of common functional loadings and a matrix-valued time series that contains the most temporal dynamics embedded in the original HDFTS. The proposed general-form dual-factor structure is connected to several commonly used functional factor models. We demonstrate the finite-sample performances of the proposed method in recovering cross-sectional basis functions and extracting common features using simulated HDFTS. An empirical study shows that the proposed model produces more accurate point and interval forecasts for subnational age-specific mortality rates in Japan. The financial benefits associated with the improved mortality forecasts are translated into a life annuity pricing scheme.This research was supported by the Australian Research Council Discovery Project (DP230102250) and Australian Research Council Future Fellowship (FT240100338).24en©2025 The authorsAge-specific mortality forecastingFactor modelsFunctional panel dataFunctional principal component analysisMultilevel functional data202510.1093/jrsssa/qnaf144105044545333