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2002-06-03Buch DOI: 10.18452/3494
A Monte Carlo Study of Structural Equation Modelsfor Finite Mixtures
dc.contributor.authorWilliams, John
dc.contributor.authorTemme, Dirk
dc.contributor.authorHildebrandt, Lutz
dc.date.accessioned2017-06-15T21:10:31Z
dc.date.available2017-06-15T21:10:31Z
dc.date.created2005-10-10
dc.date.issued2002-06-03
dc.identifier.issn1436-1086
dc.identifier.urihttp://edoc.hu-berlin.de/18452/4146
dc.description.abstractEmpirical applications of structural equation modeling (SEM) typically rest on the assumption that the analysed sample is homogenous with respect to the underlying structural model or that homogenous subsamples have been formed based on a priori knowledge. However, researchers often are ignorant about the true causes of heterogeneity and thus risk to produce misleading results. Using a sequential procedure of cluster analysis in combination with multi-group SEM has been shown to be inappropriate to solve the problem of unobserved heterogeneity. Recently, two encouraging approaches have been developed in this regard: (1) Finite mixtures of structural equation models and (2) hierarchical Bayesian estimation. In this paper, we focus exclusively on the MECOSA approach to finite normal mixtures subject to conditional mean and covariance structures. Since not much is known about the performance of MECOSA, which is both a specific odel and a software, we present the results of an extensive Monte Carlo simulation. It was found that MECOSA performed best where homogenous groups were present in the data in equal proportions and in conjunction with rather large differences in parameters across the groups. MECOSA performed worse when the proportions were unequal and parameters were relatively close together across groups. Of the three estimation methods available in MECOSA the two-stage minimum distance estimation (MDE) in general performed worse than the alternative EM algorithms (EM and EMG). This effect was especially pronounced under conditions of close parameters and unequal group proportions. Above that, for these conditions the modified likelihood ratio test turned out to be inappropriate in the three groups case.eng
dc.language.isoeng
dc.publisherHumboldt-Universität zu Berlin, Wirtschaftswissenschaftliche Fakultät
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.subject.ddc330 Wirtschaft
dc.titleA Monte Carlo Study of Structural Equation Modelsfor Finite Mixtures
dc.typebook
dc.identifier.urnurn:nbn:de:kobv:11-10049067
dc.identifier.doihttp://dx.doi.org/10.18452/3494
local.edoc.pages24
local.edoc.type-nameBuch
local.edoc.container-typeseries
local.edoc.container-type-nameSchriftenreihe
local.edoc.container-year2002
dc.identifier.zdb2135319-0
bua.series.nameSonderforschungsbereich 373: Quantification and Simulation of Economic Processes
bua.series.issuenumber2002,48

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