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
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Englisch
- Erschienen in
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Journal: Risks ; ISSN: 2227-9091 ; Volume: 9 ; Year: 2021 ; Issue: 2 ; Pages: 1-19 ; Basel: MDPI
- Klassifikation
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Wirtschaft
- Thema
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age coherent
elastic net regularization
mortality forecasting
vector autoregressive
- Ereignis
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Geistige Schöpfung
- (wer)
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Li, Hong
Shi, Yanlin
- Ereignis
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Veröffentlichung
- (wer)
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MDPI
- (wo)
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Basel
- (wann)
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2021
- DOI
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doi:10.3390/risks9020035
- Handle
- Letzte Aktualisierung
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2025-03-10T11:42:50+0100
Datenpartner
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Objekttyp
- Artikel
Beteiligte
- Li, Hong
- Shi, Yanlin
- MDPI
Entstanden
- 2021