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dc.contributor.authorArellano, Manuelen_US
dc.contributor.authorBonhomme, St├ęphaneen_US
dc.date.accessioned2012-10-16T13:15:37Z-
dc.date.available2012-10-16T13:15:37Z-
dc.date.issued2009en_US
dc.identifier.pidoi:10.1920/wp.cem.2009.2209en_US
dc.identifier.urihttp://hdl.handle.net/10419/64789-
dc.description.abstractWe study the identification of panel models with linear individual-specific coefficients, when T is fixed. We show identification of the variance of the effects under conditional uncorrelatedness. Identification requires restricted dependence of errors, reflecting a trade-off between heterogeneity and error dynamics. We show identification of the density of individual effects when errors follow an ARMA process under conditional independence. We discuss GMM estimation of moments of effects and errors, and introduce a simple density estimator of a slope effect in a special case. As an application we estimate the effect that a mother smokes during pregnancy on child's birth weight.en_US
dc.language.isoengen_US
dc.publisherCentre for Microdata Methods and Practice Londonen_US
dc.relation.ispartofseriescemmap working paper CWP22/09en_US
dc.subject.jelC23en_US
dc.subject.ddc330en_US
dc.subject.keywordpanel dataen_US
dc.subject.keywordrandom coefficientsen_US
dc.subject.keywordmultiple effectsen_US
dc.subject.keywordnonparametric identificationen_US
dc.subject.stwMathematiken_US
dc.titleIdentifying distributional characteristics in random coefficients panel data modelsen_US
dc.typeWorking Paperen_US
dc.identifier.ppn607538503en_US
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen_US
Appears in Collections:cemmap working papers, Centre for Microdata Methods and Practice, Institute for Fiscal Studies (IFS)

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