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[journal article]

dc.contributor.authorSarafidis, Vasilisde
dc.contributor.authorYamagata, Takashide
dc.contributor.authorRobertson, Donaldde
dc.date.accessioned2011-02-07T02:52:00Zde
dc.date.accessioned2012-08-30T06:49:11Z
dc.date.available2012-08-30T06:49:11Z
dc.date.issued2009de
dc.identifier.urihttp://www.ssoar.info/ssoar/handle/document/21576
dc.description.abstractThis paper proposes a new testing procedure for detecting error cross section dependence after estimating a linear dynamic panel data model with regressors using the generalised method of moments (GMM). The test is valid when the cross-sectional dimension of the panel is large relative to the time series dimension. Importantly, our approach allows one to examine whether any error cross section dependence remains after including time dummies (or after transforming the data in terms of deviations from time-specific averages), which will be the case under heterogeneous error cross section dependence. Finite sample simulation-based results suggest that our tests perform well, particularly the version based on the [Blundell, R., Bond, S., 1998. Initial conditions and moment restrictions in dynamic panel data models. Journal of Econometrics 87, 115–143] system GMM estimator. In addition, it is shown that the system GMM estimator, based only on partial instruments consisting of the regressors, can be a reliable alternative to the standard GMM estimators under heterogeneous error cross section dependence. The proposed tests are applied to employment equations using UK firm data and the results show little evidence of heterogeneous error cross section dependence.en
dc.languageende
dc.subject.ddcWirtschaftde
dc.subject.ddcSozialwissenschaften, Soziologiede
dc.subject.ddcSocial sciences, sociology, anthropologyen
dc.subject.ddcEconomicsen
dc.subject.otherCross section dependence; Generalised method of moments; Dynamic panel data; Overidentifying restrictions test; JEL classification: C12; C13; C15; C33
dc.titleA test of cross section dependence for a linear dynamic panel model with regressorsen
dc.description.reviewbegutachtet (peer reviewed)de
dc.description.reviewpeer revieweden
dc.source.journalJournal of Econometricsde
dc.source.volume148de
dc.publisher.countryNLD
dc.source.issue2de
dc.subject.classozEconomic Statistics, Econometrics, Business Informaticsen
dc.subject.classozErhebungstechniken und Analysetechniken der Sozialwissenschaftende
dc.subject.classozWirtschaftsstatistik, Ökonometrie, Wirtschaftsinformatikde
dc.subject.classozMethods and Techniques of Data Collection and Data Analysis, Statistical Methods, Computer Methodsen
dc.subject.thesozstatistical analysisen
dc.subject.thesozstatistische Analysede
dc.subject.thesozPanelde
dc.subject.thesozmodelen
dc.subject.thesozdataen
dc.subject.thesozpanelen
dc.subject.thesozModellde
dc.subject.thesozDatende
dc.identifier.urnurn:nbn:de:0168-ssoar-215764de
dc.date.modified2011-02-07T14:17:00Zde
dc.rights.licencePEER Licence Agreement (applicable only to documents from PEER project)de
dc.rights.licencePEER Licence Agreement (applicable only to documents from PEER project)en
ssoar.gesis.collectionSOLIS;ADISde
ssoar.contributor.institutionhttp://www.peerproject.eu/de
internal.status3de
internal.identifier.thesoz10035472
internal.identifier.thesoz10054018
internal.identifier.thesoz10036422
internal.identifier.thesoz10034708
dc.type.stockarticlede
dc.type.documentjournal articleen
dc.type.documentZeitschriftenartikelde
dc.rights.copyrightfde
dc.source.pageinfo149-161
internal.identifier.classoz10905
internal.identifier.classoz10105
internal.identifier.journal195de
internal.identifier.document32
internal.identifier.ddc300
internal.identifier.ddc330
dc.identifier.doihttps://doi.org/10.1016/j.jeconom.2008.10.006de
dc.description.pubstatusPostprinten
dc.description.pubstatusPostprintde
internal.identifier.licence7
internal.identifier.pubstatus2
internal.identifier.review1
internal.check.abstractlanguageharmonizerCERTAIN
internal.check.languageharmonizerCERTAIN_RETAINED


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