Academic Journal
Modelling mortality: are we heading in the right direction?
| Τίτλος: | Modelling mortality: are we heading in the right direction? |
|---|---|
| Συγγραφείς: | O'hare, Colin1 (AUTHOR), Li, Youwei2 (AUTHOR) colin.ohare@monash.edu |
| Πηγή: | Applied Economics. Jan2017, Vol. 49 Issue 2, p170-187. 18p. |
| Θεματικοί όροι: | *Mortality -- Mathematical models, *Pensions, *Insurance companies, *Structural break (Economics), *Economic forecasting, Life expectancy |
| Περίληψη: | Predicting life expectancy has become of upmost importance in society. Pension providers, insurance companies, government bodies and individuals in the developed world have a vested interest in understanding how long people will live for. This desire to better understand life expectancy has resulted in an explosion of stochastic mortality models many of which identify linear trends in mortality rates by time. In making use of such models for forecasting purposes, we rely on the assumption that the direction of the linear trend (determined from the data used for fitting purposes) will not change in the future, recent literature has started to question this assumption. In this article, we carry out a comprehensive investigation of these types of models using male and female data from 30 countries and using the theory of structural breaks to identify changes in the extracted trends by time. We find that structural breaks are present in a substantial number of cases, that they are more prevalent in male data than in female data, that the introduction of additional period factors into the model reduces their presence, and that allowing for changes in the trend improves the fit and forecast substantially. [ABSTRACT FROM PUBLISHER] |
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| Βάση Δεδομένων: | Business Source Index |
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| Items | – Name: Title Label: Title Group: Ti Data: Modelling mortality: are we heading in the right direction? – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22O'hare%2C+Colin%22">O'hare, Colin</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Youwei%22">Li, Youwei</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> colin.ohare@monash.edu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Applied+Economics%22">Applied Economics</searchLink>. Jan2017, Vol. 49 Issue 2, p170-187. 18p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Mortality+--+Mathematical+models%22">Mortality -- Mathematical models</searchLink><br />*<searchLink fieldCode="DE" term="%22Pensions%22">Pensions</searchLink><br />*<searchLink fieldCode="DE" term="%22Insurance+companies%22">Insurance companies</searchLink><br />*<searchLink fieldCode="DE" term="%22Structural+break+%28Economics%29%22">Structural break (Economics)</searchLink><br />*<searchLink fieldCode="DE" term="%22Economic+forecasting%22">Economic forecasting</searchLink><br /><searchLink fieldCode="DE" term="%22Life+expectancy%22">Life expectancy</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Predicting life expectancy has become of upmost importance in society. Pension providers, insurance companies, government bodies and individuals in the developed world have a vested interest in understanding how long people will live for. This desire to better understand life expectancy has resulted in an explosion of stochastic mortality models many of which identify linear trends in mortality rates by time. In making use of such models for forecasting purposes, we rely on the assumption that the direction of the linear trend (determined from the data used for fitting purposes) will not change in the future, recent literature has started to question this assumption. In this article, we carry out a comprehensive investigation of these types of models using male and female data from 30 countries and using the theory of structural breaks to identify changes in the extracted trends by time. We find that structural breaks are present in a substantial number of cases, that they are more prevalent in male data than in female data, that the introduction of additional period factors into the model reduces their presence, and that allowing for changes in the trend improves the fit and forecast substantially. [ABSTRACT FROM PUBLISHER] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Applied Economics 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: 10.1080/00036846.2016.1192278 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 18 StartPage: 170 Subjects: – SubjectFull: Mortality -- Mathematical models Type: general – SubjectFull: Pensions Type: general – SubjectFull: Insurance companies Type: general – SubjectFull: Structural break (Economics) Type: general – SubjectFull: Economic forecasting Type: general – SubjectFull: Life expectancy Type: general Titles: – TitleFull: Modelling mortality: are we heading in the right direction? Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: O'hare, Colin – PersonEntity: Name: NameFull: Li, Youwei IsPartOfRelationships: – BibEntity: Dates: – D: 08 M: 01 Text: Jan2017 Type: published Y: 2017 Identifiers: – Type: issn-print Value: 00036846 Numbering: – Type: volume Value: 49 – Type: issue Value: 2 Titles: – TitleFull: Applied Economics Type: main |
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