Artikel
Multivariate functional time series forecasting: Application to age-specific mortality rates
This study considers the forecasting of mortality rates in multiple populations. We propose a model that combines mortality forecasting and functional data analysis (FDA). Under the FDA framework, the mortality curve of each year is assumed to be a smooth function of age. As with most of the functional time series forecasting models, we rely on functional principal component analysis (FPCA) for dimension reduction and further choose a vector error correction model (VECM) to jointly forecast mortality rates in multiple populations. This model incorporates the merits of existing models in that it excludes some of the inherent randomness with the nonparametric smoothing from FDA, and also utilizes the correlation structures between the populations with the use of VECM in mortality models. A nonparametric bootstrap method is also introduced to construct interval forecasts. The usefulness of this model is demonstrated through a series of simulation studies and applications to the age-and sex-specific mortality rates in Switzerland and the Czech Republic. The point forecast errors of several forecasting methods are compared and interval scores are used to evaluate and compare the interval forecasts. Our model provides improved forecast accuracy in most cases.
- Language
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Englisch
- Bibliographic citation
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Journal: Risks ; ISSN: 2227-9091 ; Volume: 5 ; Year: 2017 ; Issue: 2 ; Pages: 1-18 ; Basel: MDPI
- Classification
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Wirtschaft
- Subject
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age-and sex-specific mortality rate
bootstrapping prediction interval
vector autoregressive model
vector error correction model
interval score
- Event
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Geistige Schöpfung
- (who)
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Gao, Yuan
Shang, Han Lin
- Event
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Veröffentlichung
- (who)
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MDPI
- (where)
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Basel
- (when)
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2017
- DOI
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doi:10.3390/risks5020021
- Handle
- Last update
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10.03.2025, 11:43 AM CET
Data provider
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Artikel
Associated
- Gao, Yuan
- Shang, Han Lin
- MDPI
Time of origin
- 2017
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