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2019-11-06Zeitschriftenartikel DOI: 10.18452/22233
Identifying Heterogeneity in Dynamic Panel Models with Individual Parameter Contribution Regression
dc.contributor.authorArnold, Manuel
dc.contributor.authorOberski, Daniel
dc.contributor.authorBrandmaier, Andreas
dc.contributor.authorVoelkle, Manuel
dc.date.accessioned2020-12-08T11:22:21Z
dc.date.available2020-12-08T11:22:21Z
dc.date.issued2019-11-06none
dc.identifier.other10.1080/10705511.2019.1667240
dc.identifier.urihttp://edoc.hu-berlin.de/18452/22840
dc.description.abstractDynamic panel models are a popular approach to study interrelationships between repeatedly measured variables. Often, dynamic panel models are specified and estimated within a structural equation modeling (SEM) framework. An endemic problem threatening the validity of such models is unmodelled heterogeneity. Recently, individual parameter contribution (IPC) regression was proposed as a flexible method to study heterogeneity in SEM parameters as a function of observed covariates. In the present paper, we derive how IPCs can be calculated for general maximum likelihood estimates and evaluate the performance of IPC regression to estimate group differences in dynamic panel models in discrete and continuous time. We show that IPC regression can be slightly biased in samples with large group differences and present a bias correction procedure. IPC regression showed generally promising results for discrete time models. However, due to highly nonlinear parameter constraints, caution is indicated when applying IPC regression to continuous time models.eng
dc.language.isoengnone
dc.publisherHumboldt-Universität zu Berlin
dc.rights(CC BY 4.0) Attribution 4.0 Internationalger
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectAutoregressive cross-lagged modeleng
dc.subjectcontinuous time modelingeng
dc.subjectheterogeneityeng
dc.subjectstructural equation modelingeng
dc.subject.ddc300 Sozialwissenschaftennone
dc.titleIdentifying Heterogeneity in Dynamic Panel Models with Individual Parameter Contribution Regressionnone
dc.typearticle
dc.identifier.urnurn:nbn:de:kobv:11-110-18452/22840-9
dc.identifier.doihttp://dx.doi.org/10.18452/22233
dc.type.versionpublishedVersionnone
local.edoc.container-titleStructural equation modeling : a multidisciplinary journalnone
local.edoc.pages17none
local.edoc.anmerkungThis article was supported by the Open Access Publication Fund of Humboldt-Universität zu Berlin.none
local.edoc.type-nameZeitschriftenartikel
local.edoc.institutionLebenswissenschaftliche Fakultätnone
local.edoc.container-typeperiodical
local.edoc.container-type-nameZeitschrift
local.edoc.container-publisher-namePsychology Press, Taylor & Francis Groupnone
local.edoc.container-publisher-placePhiladelphia, Pa.none
local.edoc.container-volume27none
local.edoc.container-issue4none
local.edoc.container-year2020none
local.edoc.container-firstpage613none
local.edoc.container-lastpage628none
dc.description.versionPeer Reviewednone
dc.identifier.eissn1070-5511

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