Search

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

<?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>Forecasting multiple functional time series in a group structure</title>
<subTitle>: an application to mortality</subTitle>
</titleInfo>
<name type="personal" usage="primary" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="MAPA20190015141">
<namePart>Lin Shang, Han</namePart>
<nameIdentifier>MAPA20190015141</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">bel</placeTerm>
</place>
<dateIssued encoding="marc">2020</dateIssued>
<issuance>serial</issuance>
</originInfo>
<language>
<languageTerm type="code" authority="iso639-2b">eng</languageTerm>
</language>
<physicalDescription>
<form authority="marcform">print</form>
</physicalDescription>
<abstract displayLabel="Summary">When 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.</abstract>
<note type="statement of responsibility">Han Lin Shang, Steven Haberman</note>
<subject xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="MAPA20080592011">
<topic>Modelos actuariales</topic>
</subject>
<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="MAPA20080552183">
<topic>Población</topic>
</subject>
<subject xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="MAPA20080568085">
<topic>Bases de datos</topic>
</subject>
<subject xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="MAPA20080579258">
<topic>Cálculo actuarial</topic>
</subject>
<subject authority="lcshac" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="MAPA20080650919">
<geographic>Japón</geographic>
</subject>
<classification authority="">6</classification>
<relatedItem type="host">
<titleInfo>
<title>Astin bulletin</title>
</titleInfo>
<originInfo>
<publisher>Belgium : ASTIN and AFIR Sections of the International Actuarial Association</publisher>
</originInfo>
<identifier type="issn">0515-0361</identifier>
<identifier type="local">MAP20077000420</identifier>
<part>
<text>01/05/2020 Volumen 50 Número 2 - mayo 2020 , p. 357-379</text>
</part>
</relatedItem>
<recordInfo>
<recordContentSource authority="marcorg">MAP</recordContentSource>
<recordCreationDate encoding="marc">200604</recordCreationDate>
<recordChangeDate encoding="iso8601">20200604160052.0</recordChangeDate>
<recordIdentifier source="MAP">MAP20200019053</recordIdentifier>
<languageOfCataloging>
<languageTerm type="code" authority="iso639-2b">spa</languageTerm>
</languageOfCataloging>
</recordInfo>
</mods>
</modsCollection>