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FORECASTING MULTIPLE FUNCTIONAL TIME SERIES IN A GROUP STRUCTURE: AN APPLICATION TO MORTALITY

Published online by Cambridge University Press:  18 February 2020

Han Lin Shang Open the ORCID record for Han Lin Shang [Opens in a new window]  and
Steven Haberman
Han Lin Shang*
Affiliation:
Research School of Finance, Actuarial Studies and Statistics, Australian National University, Canberra, ACT2601, Australia, E-Mail: hanlin.shang@anu.edu.au
Steven Haberman
Affiliation:
Cass Business School, City, University of London, LondonEC1V 0HB, UK, E-Mail: S.Haberman@city.ac.uk
*
E-Mail: hanlin.shang@anu.edu.au
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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.

Keywords

Forecast reconciliationmultivariate functional principal component analysisbottom-up methodoptimal combination methodJapanese mortality databaseC53C55

Information

Type
Research Article
Information
ASTIN Bulletin: The Journal of the IAA , Volume 50 , Issue 2 , May 2020 , pp. 357 - 379
Copyright
© Astin Bulletin 2020

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