Identification of Temporal Patterns in Income and LivingConditions of Czech Households: Clustering Based onMixed Type Panel Data from the EU-SILC Database

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F20%3A10414487" target="_blank" >RIV/00216208:11320/20:10414487 - isvavai.cz</a>

Výsledek na webu

<a href="https://mme2020.mendelu.cz/wcd/w-rek-mme/mme2020_conference_proceedings_final.pdf" target="_blank" >https://mme2020.mendelu.cz/wcd/w-rek-mme/mme2020_conference_proceedings_final.pdf</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Identification of Temporal Patterns in Income and LivingConditions of Czech Households: Clustering Based onMixed Type Panel Data from the EU-SILC Database

Popis výsledku v původním jazyce

The EU-SILC database contains annually gathered rotating-panel data on a household level covering indicators of monetary poverty, severe material deprivation or low work household intensity. Data are obtained via questionnaires leading to outcome variables of diverse nature: numeric, binary, ordinal being gathered at each occasion in each household. Only limited number of approaches exist in the literature to analyze such mixed-type panel data. We present a statistical model for such type of data which is built on a thresholding approach to linkbinary or ordinal variables to their latent numeric counterparts. All, possibly latent, numeric outcomes are then jointly modelled using a multivariate version of the linear mixed-effects model. A mixture of such models is then used to model heterogeneity in temporal evolution of considered outcomes across households. A Bayesian variant of the Model Based Clustering (MBC) methodology is finally exploited to classify households into groups with similar evolution of indicators of monetary poverty, material deprivation or low work household intensity. The method is applied to socially-economic focused dataset of Czech households gathered in a time span 2005-2016.

Název v anglickém jazyce

Identification of Temporal Patterns in Income and LivingConditions of Czech Households: Clustering Based onMixed Type Panel Data from the EU-SILC Database

Popis výsledku anglicky

The EU-SILC database contains annually gathered rotating-panel data on a household level covering indicators of monetary poverty, severe material deprivation or low work household intensity. Data are obtained via questionnaires leading to outcome variables of diverse nature: numeric, binary, ordinal being gathered at each occasion in each household. Only limited number of approaches exist in the literature to analyze such mixed-type panel data. We present a statistical model for such type of data which is built on a thresholding approach to linkbinary or ordinal variables to their latent numeric counterparts. All, possibly latent, numeric outcomes are then jointly modelled using a multivariate version of the linear mixed-effects model. A mixture of such models is then used to model heterogeneity in temporal evolution of considered outcomes across households. A Bayesian variant of the Model Based Clustering (MBC) methodology is finally exploited to classify households into groups with similar evolution of indicators of monetary poverty, material deprivation or low work household intensity. The method is applied to socially-economic focused dataset of Czech households gathered in a time span 2005-2016.

Druh

O - Ostatní výsledky

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

2020

Kód důvěrnosti údajů

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