Academic Journal
BIAS-CORRECTED INFERENCE FOR A MODIFIED LEE–CARTER MORTALITY MODEL.
| Τίτλος: | BIAS-CORRECTED INFERENCE FOR A MODIFIED LEE–CARTER MORTALITY MODEL. |
|---|---|
| Συγγραφείς: | Liu, Qing1 qliu8310@aliyun.com, Ling, Chen2 cling5@gsu.edu, Li, Deyuan3 deyuanli@fudan.edu.cn, Peng, Liang2 lpeng@gsu.edu |
| Πηγή: | Astin Bulletin. May2019, Vol. 49 Issue 2, p433-455. 23p. |
| Θεματικοί όροι: | *Life insurance companies, *Mortality -- Mathematical models, Death rate, Least squares, Mortality |
| Γεωγραφικοί όροι: | United States |
| Εταιρία/Οντότητα: | American Statistical Association 070110986 |
| Περίληψη: | As a benchmark mortality model in forecasting future mortality rates and hedging longevity risk, the widely employed Lee–Carter model (Lee, R.D. and Carter, L.R. (1992) Modeling and forecasting U.S. mortality. Journal of the American Statistical Association , 87 , 659–671.) suffers from a restrictive constraint on the unobserved mortality index for ensuring model's identification and a possible inconsistent inference. Recently, a modified Lee–Carter model (Liu, Q., Ling, C. and Peng, L. (2018) Statistical inference for Lee–Carter mortality model and corresponding forecasts. North American Actuarial Journal , to appear.) removes this constraint and a simple least squares estimation is consistent with a normal limit when the mortality index follows from a unit root or near unit root AR(1) model with a nonzero intercept. This paper proposes a bias-corrected estimator for this modified Lee–Carter model, which is consistent and has a normal limit regardless of the mortality index being a stationary or near unit root or unit root AR(1) process with a nonzero intercept. Applications to the US mortality rates and a simulation study are provided as well. [ABSTRACT FROM AUTHOR] |
| Copyright of Astin Bulletin is the property of Cambridge University Press 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. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Business Source Index |
| FullText | Links: – Type: other Text: Availability: 0 |
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| Header | DbId: bsx DbLabel: Business Source Index An: 136367116 RelevancyScore: 1212 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 1211.77160644531 |
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| Items | – Name: Title Label: Title Group: Ti Data: BIAS-CORRECTED INFERENCE FOR A MODIFIED LEE–CARTER MORTALITY MODEL. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Liu%2C+Qing%22">Liu, Qing</searchLink><relatesTo>1</relatesTo><i> qliu8310@aliyun.com</i><br /><searchLink fieldCode="AR" term="%22Ling%2C+Chen%22">Ling, Chen</searchLink><relatesTo>2</relatesTo><i> cling5@gsu.edu</i><br /><searchLink fieldCode="AR" term="%22Li%2C+Deyuan%22">Li, Deyuan</searchLink><relatesTo>3</relatesTo><i> deyuanli@fudan.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Peng%2C+Liang%22">Peng, Liang</searchLink><relatesTo>2</relatesTo><i> lpeng@gsu.edu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Astin+Bulletin%22">Astin Bulletin</searchLink>. May2019, Vol. 49 Issue 2, p433-455. 23p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Life+insurance+companies%22">Life insurance companies</searchLink><br />*<searchLink fieldCode="DE" term="%22Mortality+--+Mathematical+models%22">Mortality -- Mathematical models</searchLink><br /><searchLink fieldCode="DE" term="%22Death+rate%22">Death rate</searchLink><br /><searchLink fieldCode="DE" term="%22Least+squares%22">Least squares</searchLink><br /><searchLink fieldCode="DE" term="%22Mortality%22">Mortality</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22United+States%22">United States</searchLink> – Name: SubjectCompany Label: Company/Entity Group: Su Data: <searchLink fieldCode="DE" term="%22American+Statistical+Association%22">American Statistical Association</searchLink> <searchLink fieldCode="DN" term="%22070110986%22">070110986</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: As a benchmark mortality model in forecasting future mortality rates and hedging longevity risk, the widely employed Lee–Carter model (Lee, R.D. and Carter, L.R. (1992) Modeling and forecasting U.S. mortality. Journal of the American Statistical Association , 87 , 659–671.) suffers from a restrictive constraint on the unobserved mortality index for ensuring model's identification and a possible inconsistent inference. Recently, a modified Lee–Carter model (Liu, Q., Ling, C. and Peng, L. (2018) Statistical inference for Lee–Carter mortality model and corresponding forecasts. North American Actuarial Journal , to appear.) removes this constraint and a simple least squares estimation is consistent with a normal limit when the mortality index follows from a unit root or near unit root AR(1) model with a nonzero intercept. This paper proposes a bias-corrected estimator for this modified Lee–Carter model, which is consistent and has a normal limit regardless of the mortality index being a stationary or near unit root or unit root AR(1) process with a nonzero intercept. Applications to the US mortality rates and a simulation study are provided as well. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Astin Bulletin is the property of Cambridge University Press 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: 10.1017/asb.2019.9 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 23 StartPage: 433 Subjects: – SubjectFull: Life insurance companies Type: general – SubjectFull: Mortality -- Mathematical models Type: general – SubjectFull: Death rate Type: general – SubjectFull: Least squares Type: general – SubjectFull: Mortality Type: general – SubjectFull: United States Type: general – SubjectFull: American Statistical Association Type: general Titles: – TitleFull: BIAS-CORRECTED INFERENCE FOR A MODIFIED LEE–CARTER MORTALITY MODEL. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Liu, Qing – PersonEntity: Name: NameFull: Ling, Chen – PersonEntity: Name: NameFull: Li, Deyuan – PersonEntity: Name: NameFull: Peng, Liang IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2019 Type: published Y: 2019 Identifiers: – Type: issn-print Value: 05150361 Numbering: – Type: volume Value: 49 – Type: issue Value: 2 Titles: – TitleFull: Astin Bulletin Type: main |
| ResultId | 1 |