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Coherent mortality forecasts for a group of populations: An extension of the lee-carter method
Open accessAbstract
Mortality patterns and trajectories in closely related populations are likely to be similar in some respects, and differences are unlikely to increase in the long run. It should therefore be possible to improve the mortality forecasts for individual countries by taking into account the patterns in a larger group. Using the Human Mortality Database, we apply the Lee-Carter model to a group of populations, allowing each its own age pattern and level of mortality but imposing shared rates of change by age. Our forecasts also allow divergent patterns to continue for a while before tapering off. We forecast greater longevity gains for the United States and lesser ones for Japan relative to separate forecasts.
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Citations
Cited by
The outlook for population growth
Lee, R. (2011) · Science · doi:10.1126/science.1208859 · Cited by 415
Self-citationForecasting life expectancy in an international context
Torri, T., & Vaupel, J. W. (2012) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2011.01.009 · Cited by 69
Bravo, J. M., & Ayuso, M. (2021) · Mathematics · doi:10.3390/math9243307 · Cited by 18
Minimum Death Rates and Maximum Life Expectancy: The Role of Concordant Ages
Canudas-Romo, V., Booth, H., & Bergeron-Boucher, M.-P. (2019) · North American Actuarial Journal · doi:10.1080/10920277.2018.1519448 · Cited by 12
Metadata
- Authors
- Li, N., & Lee, R.
- Venue
- Demography
- Publisher
- Duke University Press
- Published
- 2005-08-01
- Volume
- 42
- Issue
- 3
- Pages
- 575-594
- ISSN
- 0070-3370 (ISSN-L)
- 1533-7790
- OpenAlex
- W2049781056
- Cited by
- 622
- References
- 0
- FWCI
- 11.29
- Type
- journal article
- Language
- en
- Open access
- bronze
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
