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hal.structure.identifierLaboratoire de Sciences Actuarielle et Financière [SAF]
hal.structure.identifierCEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
dc.contributor.authorGuibert, Quentin
HAL ID: 3858
ORCID: 0000-0002-4915-2422
hal.structure.identifier
dc.contributor.authorLopez, Olivier
HAL ID: 737418
ORCID: 0000-0003-0818-4564
hal.structure.identifierLaboratoire de Probabilités, Statistique et Modélisation [LPSM (UMR_8001)]
dc.contributor.authorPiette, Pierrick
HAL ID: 15420
ORCID: 0000-0002-2769-5880
dc.date.accessioned2019-09-04T13:11:36Z
dc.date.available2019-09-04T13:11:36Z
dc.date.issued2019
dc.identifier.issn1873-5959
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/19686
dc.language.isoenen
dc.subjectMortality forecastingen
dc.subjectHigh-dimensional time seriesen
dc.subjectVector autoregressionen
dc.subjectElastic-neten
dc.subjectAge-cohort effecten
dc.subject.ddc515en
dc.subject.classificationjelC.C1.C18en
dc.subject.classificationjelC.C3.C32en
dc.subject.classificationjelC.C5.C52en
dc.subject.classificationjelC.C5.C53en
dc.titleForecasting mortality rate improvements with a high-dimensional VARen
dc.typeArticle accepté pour publication ou publié
dc.description.abstractenForecasting mortality rates is a problem which involves the analysis of high-dimensional time series. Most of usual mortality models propose to decompose the mortality rates into several latent factors to reduce this complexity. These approaches, in particular those using cohort factors, have a good fit, but they are less reliable for forecasting purposes. One of the major challenges is to determine the spatial–temporal dependence structure between mortality rates given a relatively moderate sample size. This paper proposes a large vector autoregressive (VAR) model fitted on the differences in the log-mortality rates, ensuring the existence of long-run relationships between mortality rate improvements. Our contribution is threefold. First, sparsity, when fitting the model, is ensured by using high-dimensional variable selection techniques without imposing arbitrary constraints on the dependence structure. The main interest is that the structure of the model is directly driven by the data, in contrast to the main factor-based mortality forecasting models. Hence, this approach is more versatile and would provide good forecasting performance for any considered population. Additionally, our estimation allows a one-step procedure, as we do not need to estimate hyper-parameters. The variance–covariance matrix of residuals is then estimated through a parametric form. Secondly, our approach can be used to detect nonintuitive age dependence in the data, beyond the cohort and the period effects which are implicitly captured by our model. Third, our approach can be extended to model the several populations in long run perspectives, without raising issue in the estimation process. Finally, in an out-of-sample forecasting study for mortality rates, we obtain rather good performances and more relevant forecasts compared to classical mortality models using the French, US and UK data. We also show that our results enlighten the so-called cohort and period effects for these populations.en
dc.relation.isversionofjnlnameInsurance. Mathematics and Economics
dc.relation.isversionofjnlvol88en
dc.relation.isversionofjnldate2019-09
dc.relation.isversionofjnlpages255-272en
dc.relation.isversionofdoi10.1016/j.insmatheco.2019.07.004en
dc.subject.ddclabelAnalyseen
dc.relation.forthcomingnonen
dc.relation.forthcomingprintnonen
dc.description.ssrncandidatenonen
dc.description.halcandidatenonen
dc.description.readershiprechercheen
dc.description.audienceInternationalen
dc.relation.Isversionofjnlpeerreviewedouien
dc.relation.Isversionofjnlpeerreviewedouien
dc.date.updated2019-09-04T13:04:08Z
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hal.author.functionaut
hal.author.functionaut


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