A Bayesian model for age at death with cohort effects
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
A Bayesian model for age at death with cohort effects
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10103 - Statistics and probability
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Demographic Research
ISSN
1435-9871
e-ISSN
1435-9871
Volume of the periodical
51
Issue of the periodical within the volume
October 2024
Country of publishing house
DE - GERMANY
Number of pages
42
Pages from-to
1017-1058
UT code for WoS article
001343368300001
EID of the result in the Scopus database
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