Search

Modelling and forecasting mortality improvement rates with random effects

<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.loc.gov/MARC21/slim http://www.loc.gov/standards/marcxml/schema/MARC21slim.xsd">
  <record>
    <leader>00000cab a2200000   4500</leader>
    <controlfield tag="001">MAP20220007948</controlfield>
    <controlfield tag="003">MAP</controlfield>
    <controlfield tag="005">20220310171128.0</controlfield>
    <controlfield tag="008">220310e20211206esp|||p      |0|||b|spa d</controlfield>
    <datafield tag="040" ind1=" " ind2=" ">
      <subfield code="a">MAP</subfield>
      <subfield code="b">spa</subfield>
      <subfield code="d">MAP</subfield>
    </datafield>
    <datafield tag="084" ind1=" " ind2=" ">
      <subfield code="a">341</subfield>
    </datafield>
    <datafield tag="100" ind1="1" ind2=" ">
      <subfield code="0">MAPA20110016920</subfield>
      <subfield code="a">Renshaw, Arthur</subfield>
    </datafield>
    <datafield tag="245" ind1="1" ind2="0">
      <subfield code="a">Modelling and forecasting mortality improvement rates with random effects</subfield>
      <subfield code="c">Arthur Renshaw, Steven Haberman</subfield>
    </datafield>
    <datafield tag="520" ind1=" " ind2=" ">
      <subfield code="a">A 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</subfield>
    </datafield>
    <datafield tag="540" ind1=" " ind2=" ">
      <subfield code="a">La copia digital se distribuye bajo licencia "Attribution 4.0 International (CC BY 4.0)"</subfield>
      <subfield code="u">https://creativecommons.org/licenses/by/4.0</subfield>
      <subfield code="9">43</subfield>
    </datafield>
    <datafield tag="650" ind1=" " ind2="4">
      <subfield code="0">MAPA20080555306</subfield>
      <subfield code="a">Mortalidad</subfield>
    </datafield>
    <datafield tag="650" ind1=" " ind2="4">
      <subfield code="0">MAPA20080592011</subfield>
      <subfield code="a">Modelos actuariales</subfield>
    </datafield>
    <datafield tag="700" ind1="1" ind2=" ">
      <subfield code="0">MAPA20080165116</subfield>
      <subfield code="a">Haberman, Steven</subfield>
    </datafield>
    <datafield tag="773" ind1="0" ind2=" ">
      <subfield code="w">MAP20220007085</subfield>
      <subfield code="g">06/12/2021 Volúmen 11 - Número 2 - diciembre 2021 , p. 381-412</subfield>
      <subfield code="t">European Actuarial Journal</subfield>
      <subfield code="d">Cham, Switzerland  : Springer Nature Switzerland AG,  2021-2022</subfield>
    </datafield>
    <datafield tag="856" ind1=" " ind2=" ">
      <subfield code="q">application/pdf</subfield>
      <subfield code="w">1114467</subfield>
      <subfield code="y">Recurso electrónico / Electronic resource</subfield>
    </datafield>
  </record>
</collection>