Forecasting multiple functional time series in a group structure : an application to mortality
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Tag | 1 | 2 | Valor |
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LDR | 00000cab a2200000 4500 | ||
001 | MAP20200019053 | ||
003 | MAP | ||
005 | 20200604160052.0 | ||
008 | 200604e20200501bel|||p |0|||b|eng d | ||
040 | $aMAP$bspa$dMAP | ||
084 | $a6 | ||
100 | 1 | $0MAPA20190015141$aLin Shang, Han | |
245 | 1 | 0 | $aForecasting multiple functional time series in a group structure$b: an application to mortality$cHan Lin Shang, Steven Haberman |
520 | $aWhen modelling subnational mortality rates, we should consider three features: How to incorporate any possible correlation among subpopulations to potentially improve forecast accuracy through multi-population joint modelling; How to reconcile subnational mortality forecasts so that they aggregate adequately across various levels of a group structure; Among the forecast reconciliation methods, how to combine their forecasts to achieve improved forecast accuracy. To address these issues, we introduce an extension of grouped univariate functional time-series method. We first consider a multivariate functional time-series method to jointly forecast multiple related series. We then evaluate the impact and benefit of using forecast combinations among the forecast reconciliation methods. Using the Japanese regional age-specific mortality rates, we investigate 115-step-ahead point and interval forecast accuracies of our proposed extension and make recommendations. | ||
650 | 4 | $0MAPA20080592011$aModelos actuariales | |
650 | 4 | $0MAPA20080555306$aMortalidad | |
650 | 4 | $0MAPA20080552183$aPoblación | |
650 | 4 | $0MAPA20080568085$aBases de datos | |
650 | 4 | $0MAPA20080579258$aCálculo actuarial | |
651 | 1 | $0MAPA20080650919$aJapón | |
700 | 1 | $0MAPA20080165116$aHaberman, Steven | |
773 | 0 | $wMAP20077000420$tAstin bulletin$dBelgium : ASTIN and AFIR Sections of the International Actuarial Association$x0515-0361$g01/05/2020 Volumen 50 Número 2 - mayo 2020 , p. 357-379 |