The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Applying Lee-Carter under conditions of variable mortality decline
Abstract
The Lee-Carter method of mortality forecasting assumes an invariant age component and most applications have adopted a linear time component. The use of the method with Australian data is compromised by significant departures from linearity in the time component and changes over time in the age component. We modify the method to adjust the time component to reproduce the age distribution of deaths, rather than total deaths, and to determine the optimal fitting period in order to address non-linearity in the time component. In the Australian case the modification has the added advantage that the assumption of invariance is better met. For Australian data, the modifications result in higher forecast life expectancy than the original Lee-Carter method and official projections, and a 50 per cent reduction in forecast error. The model is also expanded to take account of age-time interactions by incorporating additional terms, but these are not readily incorporated into forecasts.
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Golub, G. (2012) · doi:10.56021/9781421407944 · Cited by 34,505
Trefethen, L. N., & Bau, III (1997) · doi:10.1137/1.9780898719574 · Cited by 3,301
Metadata
- Authors
- BOOTH, H., MAINDONALD, J., & SMITH, L.
- Venue
- Population Studies
- Publisher
- Informa UK Limited
- Published
- 2002-01
- Volume
- 56
- Issue
- 3
- Pages
- 325-336
- ISSN
- 0032-4728 (ISSN-L)
- 1477-4747
- OpenAlex
- W2040375181
- Cited by
- 454
- References
- 2
- FWCI
- 18.99
- Type
- journal article
- Language
- en
- Open access
- closed
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- BOOTH, H., MAINDONALD, J., & SMITH, L. (2002). Applying Lee-Carter under conditions of variable mortality decline. Population Studies. https://doi.org/10.1080/00324720215935
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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.
