Academic Journal
Modelling and forecasting adult age-at-death distributions.
| Τίτλος: | Modelling and forecasting adult age-at-death distributions. |
|---|---|
| Συγγραφείς: | Basellini, Ugofilippo1,2, Camarda, Carlo Giovanni1 |
| Πηγή: | Population Studies. Mar2019, Vol. 73 Issue 1, p119-138. 20p. 5 Charts, 8 Graphs. |
| Θεματικοί όροι: | *Death forecasting, *Mortality -- Mathematical models, *Multiple correspondence analysis (Statistics), Life expectancy, Longevity |
| Περίληψη: | Age-at-death distributions provide an informative description of the mortality pattern of a population but have generally been neglected for modelling and forecasting mortality. In this paper, we use the distribution of deaths to model and forecast adult mortality. Specifically, we introduce a relational model that relates a fixed 'standard' to a series of observed distributions by a transformation of the age axis. The proposed Segmented Transformation Age-at-death Distributions (STAD) model is parsimonious and efficient: using only three parameters, it captures and disentangles mortality developments in terms of shifting and compression dynamics. Additionally, mortality forecasts can be derived from parameter extrapolation using time-series models. We illustrate our method and compare it with the Lee-Carter model and variants for females in four high-longevity countries. We show that the STAD fits the observed mortality pattern very well, and that its forecasts are more accurate and optimistic than the Lee-Carter variants. [ABSTRACT FROM AUTHOR] |
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| Items | – Name: Title Label: Title Group: Ti Data: Modelling and forecasting adult age-at-death distributions. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Basellini%2C+Ugofilippo%22">Basellini, Ugofilippo</searchLink><relatesTo>1,2</relatesTo><br /><searchLink fieldCode="AR" term="%22Camarda%2C+Carlo+Giovanni%22">Camarda, Carlo Giovanni</searchLink><relatesTo>1</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Population+Studies%22">Population Studies</searchLink>. Mar2019, Vol. 73 Issue 1, p119-138. 20p. 5 Charts, 8 Graphs. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Death+forecasting%22">Death forecasting</searchLink><br />*<searchLink fieldCode="DE" term="%22Mortality+--+Mathematical+models%22">Mortality -- Mathematical models</searchLink><br />*<searchLink fieldCode="DE" term="%22Multiple+correspondence+analysis+%28Statistics%29%22">Multiple correspondence analysis (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Life+expectancy%22">Life expectancy</searchLink><br /><searchLink fieldCode="DE" term="%22Longevity%22">Longevity</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Age-at-death distributions provide an informative description of the mortality pattern of a population but have generally been neglected for modelling and forecasting mortality. In this paper, we use the distribution of deaths to model and forecast adult mortality. Specifically, we introduce a relational model that relates a fixed 'standard' to a series of observed distributions by a transformation of the age axis. The proposed Segmented Transformation Age-at-death Distributions (STAD) model is parsimonious and efficient: using only three parameters, it captures and disentangles mortality developments in terms of shifting and compression dynamics. Additionally, mortality forecasts can be derived from parameter extrapolation using time-series models. We illustrate our method and compare it with the Lee-Carter model and variants for females in four high-longevity countries. We show that the STAD fits the observed mortality pattern very well, and that its forecasts are more accurate and optimistic than the Lee-Carter variants. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Population Studies is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1080/00324728.2018.1545918 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 20 StartPage: 119 Subjects: – SubjectFull: Death forecasting Type: general – SubjectFull: Mortality -- Mathematical models Type: general – SubjectFull: Multiple correspondence analysis (Statistics) Type: general – SubjectFull: Life expectancy Type: general – SubjectFull: Longevity Type: general Titles: – TitleFull: Modelling and forecasting adult age-at-death distributions. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Basellini, Ugofilippo – PersonEntity: Name: NameFull: Camarda, Carlo Giovanni IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: Mar2019 Type: published Y: 2019 Identifiers: – Type: issn-print Value: 00324728 Numbering: – Type: volume Value: 73 – Type: issue Value: 1 Titles: – TitleFull: Population Studies Type: main |
| ResultId | 1 |