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Forecasting multiple functional time series in a group structure: an application to mortality’

Shang, H.L. & Haberman, S. ORCID: 0000-0003-2269-9759 (2020). Forecasting multiple functional time series in a group structure: an application to mortality’. ASTIN Bulletin, doi: 10.1017/asb.2020.3


When modeling sub-national mortality rates, we should consider three features: (1) how to incorporate any possible correlation among sub-populations to potentially improve forecast accuracy through multi-population joint modeling; (2) how to reconcile sub-national mortality forecasts so that they aggregate adequately across various levels of a group structure; (3) among the forecast reconciliation methods, how to combine their forecasts to achieve improved forecast accuracy. To address these issues, we introduce an extension of grouped univariate functional time series method. We first consider a multivariate functional time series method to jointly forecast multiple related series. We then evaluate the impact and benefit of using forecast combinations among the forecast reconciliation methods. Using the Japanese regional age-specific mortality rates, we investigate one-step-ahead to 15-step-ahead point and interval forecast accuracies of our proposed extension and make recommendations.

Publication Type: Article
Additional Information: This article is to be published in a revised form in ASTIN Bulletin ( This version is free to view and download for private research and study only. Not for re-distribution or re-use. © Astin Bulletin 2020.
Publisher Keywords: forecast reconciliation; multivariate functional principal component analysis; bottomup method; optimal-combination method; Japanese mortality database
Subjects: H Social Sciences > HF Commerce
Departments: Bayes Business School > Actuarial Science & Insurance
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