FORECASTING MULTIPLE FUNCTIONAL TIME SERIES IN A GROUP STRUCTURE: AN APPLICATION TO MORTALITY. Issue 2 (18th May 2020)

• Record Type: Journal Article
• Authors: Shang, Han Lin; Haberman, Steven
• Is Part Of: ASTIN bulletin. Volume 50:Issue 2(2020)
• Page Start: 357
• Page End: 379
• Publication Date: 2020-05-18
• Subjects: Forecast reconciliation, -- multivariate functional principal component analysis, -- bottom-up method, -- optimal combination method, -- Japanese mortality database; C53, -- C55; Insurance -- Mathematics -- Periodicals; Risk (Insurance) -- Mathematics -- Periodicals; 368.01
• DOI: 10.1017/asb.2020.3
• Languages: English
• ISSNs: 0515-0361

Abstract

Abstract: When modelling subnational mortality rates, we should consider three features: (1) how to incorporate any possible correlation among subpopulations to potentially improve forecast accuracy through multi-population joint modelling; (2) how to reconcile subnational 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 1–15-step-ahead point and interval forecast accuracies of our proposed extension and make recommendations.