A Bayesian model for age at death with cohort effects

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F24%3A00601544" target="_blank" >RIV/67985807:_____/24:00601544 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.4054/DemRes.2024.51.33" target="_blank" >https://doi.org/10.4054/DemRes.2024.51.33</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.4054/DemRes.2024.51.33" target="_blank" >10.4054/DemRes.2024.51.33</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A Bayesian model for age at death with cohort effects

Popis výsledku v původním jazyce

BACKGROUND: Ongoing mortality trends affect the distribution of age at death, typically described by parametric models. Cohort effects can markedly perturb the distribution and reduce the fit of such models, and this needs to be specifically taken into account. OBJECTIVE: This study examines the integration of cohort effects in a three-component parametric model for the age-at-death distribution, applying it to data with significant cohort effects. METHODS: We employed a mixture model with a half-normal and two skew-normal components, adapted to a Bayesian framework to include multiplicative cohort effects. The model was applied to data from five Italian regions, with cohort effects estimated for the 1915-1925 cohorts. RESULTS: Incorporating cohort effects significantly improved the model's fit. A notable finding of the comprehensive model is the shift in Italy from premature to middle-age mortality components over time. Our results also demonstrate the tendency for mortality structures to spatially homogenize over time in Italy. CONCLUSIONS: The study underscores the importance of including cohort effects in mortality models in order to provide a more detailed picture of mortality trends. CONTRIBUTION: This work introduces a novel application of a Bayesian mixture model with cohort effects, offering enhanced tools for demographic analysis and new insights into the evolution of mortality components in Italy. This approach is general but fully formalized and hence it can be readily used for demographic studies in other regions as well.

Název v anglickém jazyce

A Bayesian model for age at death with cohort effects

Popis výsledku anglicky

BACKGROUND: Ongoing mortality trends affect the distribution of age at death, typically described by parametric models. Cohort effects can markedly perturb the distribution and reduce the fit of such models, and this needs to be specifically taken into account. OBJECTIVE: This study examines the integration of cohort effects in a three-component parametric model for the age-at-death distribution, applying it to data with significant cohort effects. METHODS: We employed a mixture model with a half-normal and two skew-normal components, adapted to a Bayesian framework to include multiplicative cohort effects. The model was applied to data from five Italian regions, with cohort effects estimated for the 1915-1925 cohorts. RESULTS: Incorporating cohort effects significantly improved the model's fit. A notable finding of the comprehensive model is the shift in Italy from premature to middle-age mortality components over time. Our results also demonstrate the tendency for mortality structures to spatially homogenize over time in Italy. CONCLUSIONS: The study underscores the importance of including cohort effects in mortality models in order to provide a more detailed picture of mortality trends. CONTRIBUTION: This work introduces a novel application of a Bayesian mixture model with cohort effects, offering enhanced tools for demographic analysis and new insights into the evolution of mortality components in Italy. This approach is general but fully formalized and hence it can be readily used for demographic studies in other regions as well.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

10103 - Statistics and probability

Projekt

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Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Demographic Research

ISSN

1435-9871

e-ISSN

1435-9871

Svazek periodika

51

Číslo periodika v rámci svazku

October 2024

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

42

Strana od-do

1017-1058

Kód UT WoS článku

001343368300001

EID výsledku v databázi Scopus

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