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Forecasting multiple functional time series in a group structure : an application to mortality

Recurso electrónico / Electronic resource
Registro MARC
Tag12Valor
LDR  00000cab a2200000 4500
001  MAP20200019053
003  MAP
005  20200604160052.0
008  200604e20200501bel|||p |0|||b|eng d
040  ‎$a‎MAP‎$b‎spa‎$d‎MAP
084  ‎$a‎6
1001 ‎$0‎MAPA20190015141‎$a‎Lin Shang, Han
24510‎$a‎Forecasting multiple functional time series in a group structure‎$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
7001 ‎$0‎MAPA20080165116‎$a‎Haberman, Steven
7730 ‎$w‎MAP20077000420‎$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