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dc.contributor.authorAlexopoulos, Angelis
dc.contributor.authorDellaportas, P
dc.contributor.authorForster, JJ
dc.date.accessioned2018-12-20T00:31:16Z
dc.date.available2018-12-20T00:31:16Z
dc.date.issued2019-02
dc.identifier.issn0964-1998
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/287240
dc.description.abstractWe provide forecasts for mortality rates by using two different approaches. First we employ dynamic non-linear logistic models based on Heligman-Pollard formula. Second, we assume that the dynamics of the mortality rates can be modelled through a Gaussian Markov random field. We use efficient Bayesian methods to estimate the parameters and the latent states of the proposed models. Both methodologies are tested with past data and are used to forecast mortality rates both for large (UK and Wales) and small (New Zealand) populations up to 21 years ahead. We demonstrate that predictions for individual survivor functions and other posterior summaries of demographic and actuarial interest are readily obtained. Our results are compared with other competing forecasting methods.
dc.publisherWiley
dc.rightsAttribution-NonCommercial 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/
dc.titleBayesian forecasting of mortality rates by using latent Gaussian models
dc.typeArticle
prism.endingPage711
prism.issueIdentifier2
prism.publicationDate2019
prism.publicationNameJournal of the Royal Statistical Society. Series A: Statistics in Society
prism.startingPage689
prism.volume182
dc.identifier.doi10.17863/CAM.34547
dcterms.dateAccepted2018-10-01
rioxxterms.versionofrecord10.1111/rssa.12422
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2019-02-01
dc.contributor.orcidAlexopoulos, Angelis [0000-0002-5723-6570]
dc.identifier.eissn1467-985X
dc.publisher.urlhttp://dx.doi.org/10.1111/rssa.12422
rioxxterms.typeJournal Article/Review
cam.issuedOnline2018-11-20


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Attribution-NonCommercial 4.0 International
Except where otherwise noted, this item's licence is described as Attribution-NonCommercial 4.0 International