Artikel

Mortality forecasting with an age-coherent sparse VAR model

This paper proposes an age-coherent sparse Vector Autoregression mortality model, which combines the appealing features of existing VAR-based mortality models, to forecast future mortality rates. In particular, the proposed model utilizes a data-driven method to determine the autoregressive coefficient matrix, and then employs a rotation algorithm in the projection phase to generate age-coherent mortality forecasts. In the estimation phase, the age-specific mortality improvement rates are fitted to a VAR model with dimension reduction algorithms such as the elastic net. In the projection phase, the projected mortality improvement rates are assumed to follow a short-term fluctuation component and a long-term force of decay, and will eventually converge to an age-invariant mean in expectation. The age-invariance of the long-term mean guarantees age-coherent mortality projections. The proposed model is generalized to multi-population context in a computationally efficient manner. Using single-age, uni-sex mortality data of the UK and France, we show that the proposed model is able to generate more reasonable long-term projections, as well as more accurate short-term out-of-sample forecasts than popular existing mortality models under various settings. Therefore, the proposed model is expected to be an appealing alternative to existing mortality models in insurance and demographic analyses.

Sprache
Englisch

Erschienen in
Journal: Risks ; ISSN: 2227-9091 ; Volume: 9 ; Year: 2021 ; Issue: 2 ; Pages: 1-19 ; Basel: MDPI

Klassifikation
Wirtschaft
Thema
age coherent
elastic net regularization
mortality forecasting
vector autoregressive

Ereignis
Geistige Schöpfung
(wer)
Li, Hong
Shi, Yanlin
Ereignis
Veröffentlichung
(wer)
MDPI
(wo)
Basel
(wann)
2021

DOI
doi:10.3390/risks9020035
Handle
Letzte Aktualisierung
2025-03-10T11:42:50+0100

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Artikel

Beteiligte

  • Li, Hong
  • Shi, Yanlin
  • MDPI

Entstanden

  • 2021

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