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Model confidence sets and forecast combination: An application to age-specific mortality

Shang, Hanlin; Haberman, Steven

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Background: Model averaging combines forecasts obtained from a range of models, and it often produces more accurate forecasts than a forecast from a single model. Objective: The crucial part of forecast accuracy improvement in using the model averaging lies in the determination of optimal weights from a finite sample. If the weights are selected sub-optimally, this can affect the accuracy of the model-averaged forecasts. Instead of choosing the optimal weights, we consider trimming a set of...[Show more]

dc.contributor.authorShang, Hanlin
dc.contributor.authorHaberman, Steven
dc.date.accessioned2019-09-27T05:38:45Z
dc.date.available2019-09-27T05:38:45Z
dc.identifier.issn2035-5556
dc.identifier.urihttp://hdl.handle.net/1885/172038
dc.description.abstractBackground: Model averaging combines forecasts obtained from a range of models, and it often produces more accurate forecasts than a forecast from a single model. Objective: The crucial part of forecast accuracy improvement in using the model averaging lies in the determination of optimal weights from a finite sample. If the weights are selected sub-optimally, this can affect the accuracy of the model-averaged forecasts. Instead of choosing the optimal weights, we consider trimming a set of models before equally averaging forecasts from the selected superior models. Motivated by Hansen et al. (2011), we apply and evaluate the model confidence set procedure when combining mortality forecasts. Data & Methods: The proposed model averaging procedure is motivated by Samuels and Sekkel (2017) based on the concept of model confidence sets as proposed by Hansen et al. (2011) that incorporates the statistical significance of the forecasting performance. As the model confidence level increases, the set of superior models generally decreases. The proposed model averaging procedure is demonstrated via national and sub-national Japanese mortality for retirement ages between 60 and 100+. Results: Illustrated by national and sub-national Japanese mortality for ages between 60 and 100+, the proposed model-average procedure gives the smallest interval forecast errors, especially for males. Conclusion: We find that robust out-of-sample point and interval forecasts may be obtained from the trimming method. By robust, we mean robustness against model misspecification.
dc.format.mimetypeapplication/pdf
dc.language.isoen_AU
dc.publisherSpringerOpen
dc.rights© 2018 The Author(s)
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.sourceGenus - Journal of Population Sciences
dc.subjectEqual predictability test
dc.subjectJapanese human mortality database
dc.subjectMean interval score
dc.subjectModel averaging
dc.subjectRoot mean square forecast error
dc.titleModel confidence sets and forecast combination: An application to age-specific mortality
dc.typeJournal article
local.description.notesImported from ARIES
local.identifier.citationvolume74
dc.date.issued2018-11-21
local.identifier.absfor010401 - Applied Statistics
local.identifier.absfor160304 - Mortality
local.identifier.ariespublicationu1027566xPUB113
local.publisher.urlhttps://genus.springeropen.com
local.type.statusPublished Version
local.contributor.affiliationShang, Hanlin, College of Business and Economics, ANU
local.contributor.affiliationHaberman, Steven, City University of London
local.bibliographicCitation.issue19
local.bibliographicCitation.startpage1
local.bibliographicCitation.lastpage23
local.identifier.doi10.1186/s41118-018-0043-9
local.identifier.absseo970101 - Expanding Knowledge in the Mathematical Sciences
dc.date.updated2019-04-21T08:24:01Z
local.identifier.scopusID2-s2.0-85057064691
dcterms.accessRightsOpen Access
dc.provenances This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
dc.rights.licenseCreative Commons Attribution 4.0 International License
CollectionsANU Research Publications

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