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Modelling and forecasting mortality improvement rates with random effects

Modelling and forecasting mortality improvement rates with random effects
Recurso electrónico / Electronic resource
MARC record
Tag12Value
LDR  00000cab a2200000 4500
001  MAP20220007948
003  MAP
005  20220310171128.0
008  220310e20211206esp|||p |0|||b|spa d
040  ‎$a‎MAP‎$b‎spa‎$d‎MAP
084  ‎$a‎341
1001 ‎$0‎MAPA20110016920‎$a‎Renshaw, Arthur
24510‎$a‎Modelling and forecasting mortality improvement rates with random effects‎$c‎Arthur Renshaw, Steven Haberman
520  ‎$a‎A common feature in the modelling and extrapolation of the trends in mortality rates over time, based on fitted parametric structures, has tended to involve the treatment of a structured fitted main effects period component (with possibly a cohort component) as a random effects time series. In this paper, we follow the lead of Haberman and Renshaw (Insurance Math Econ 50:309333, 2012) and other authors in modelling and forecasting mortality improvement rates over time, rather than mortality rates. In this context, we assume linear parametric structures for mortality improvement rates, and we examine the feasibility of modelling the main period effects (and possibly any cohort effects) as a random effect from the outset. We argue that this leads to a more unified approach to model fitting and extrapolation
540  ‎$a‎La copia digital se distribuye bajo licencia "Attribution 4.0 International (CC BY 4.0)"‎$u‎https://creativecommons.org/licenses/by/4.0‎$9‎43
650 4‎$0‎MAPA20080555306‎$a‎Mortalidad
650 4‎$0‎MAPA20080592011‎$a‎Modelos actuariales
7001 ‎$0‎MAPA20080165116‎$a‎Haberman, Steven
7730 ‎$w‎MAP20220007085‎$g‎06/12/2021 Volúmen 11 - Número 2 - diciembre 2021 , p. 381-412‎$t‎European Actuarial Journal‎$d‎Cham, Switzerland : Springer Nature Switzerland AG, 2021-2022
856  ‎$q‎application/pdf‎$w‎1114467‎$y‎Recurso electrónico / Electronic resource