Search

Modelling and forecasting mortality improvement rates with random effects

<?xml version="1.0" encoding="UTF-8"?><modsCollection xmlns="http://www.loc.gov/mods/v3" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-8.xsd">
<mods version="3.8">
<titleInfo>
<title>Modelling and forecasting mortality improvement rates with random effects</title>
</titleInfo>
<name type="personal" usage="primary" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="MAPA20110016920">
<namePart>Renshaw, Arthur</namePart>
<nameIdentifier>MAPA20110016920</nameIdentifier>
</name>
<name type="personal" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="MAPA20080165116">
<namePart>Haberman, Steven</namePart>
<nameIdentifier>MAPA20080165116</nameIdentifier>
</name>
<typeOfResource>text</typeOfResource>
<genre authority="marcgt">periodical</genre>
<originInfo>
<place>
<placeTerm type="code" authority="marccountry">esp</placeTerm>
</place>
<dateIssued encoding="marc">2021</dateIssued>
<issuance>serial</issuance>
</originInfo>
<language>
<languageTerm type="code" authority="iso639-2b">spa</languageTerm>
</language>
<physicalDescription>
<form authority="marcform">print</form>
<internetMediaType>application/pdf</internetMediaType>
</physicalDescription>
<abstract displayLabel="Summary">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</abstract>
<accessCondition type="use and reproduction">La copia digital se distribuye bajo licencia "Attribution 4.0 International (CC BY 4.0)"</accessCondition>
<note type="statement of responsibility">Arthur Renshaw, Steven Haberman</note>
<subject xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="MAPA20080555306">
<topic>Mortalidad</topic>
</subject>
<subject xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="MAPA20080592011">
<topic>Modelos actuariales</topic>
</subject>
<classification authority="">341</classification>
<relatedItem type="host">
<titleInfo>
<title>European Actuarial Journal</title>
</titleInfo>
<originInfo>
<publisher>Cham, Switzerland  : Springer Nature Switzerland AG,  2021-2022</publisher>
</originInfo>
<identifier type="local">MAP20220007085</identifier>
<part>
<text>06/12/2021 Número 2 - diciembre 2021 , p. 381-412</text>
</part>
</relatedItem>
<recordInfo>
<recordContentSource authority="marcorg">MAP</recordContentSource>
<recordCreationDate encoding="marc">220310</recordCreationDate>
<recordChangeDate encoding="iso8601">20220310171128.0</recordChangeDate>
<recordIdentifier source="MAP">MAP20220007948</recordIdentifier>
<languageOfCataloging>
<languageTerm type="code" authority="iso639-2b">spa</languageTerm>
</languageOfCataloging>
</recordInfo>
</mods>
</modsCollection>