Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/64789 
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dc.contributor.authorArellano, Manuelen
dc.contributor.authorBonhomme, Stéphaneen
dc.date.accessioned2012-10-16T13:15:37Z-
dc.date.available2012-10-16T13:15:37Z-
dc.date.issued2009-
dc.identifier.pidoi:10.1920/wp.cem.2009.2209en
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
dc.language.isoengen
dc.publisher|aCentre for Microdata Methods and Practice (cemmap) |cLondonen
dc.relation.ispartofseries|acemmap working paper |xCWP22/09en
dc.subject.jelC23en
dc.subject.ddc330en
dc.subject.keywordpanel dataen
dc.subject.keywordrandom coefficientsen
dc.subject.keywordmultiple effectsen
dc.subject.keywordnonparametric identificationen
dc.subject.stwMathematiken
dc.titleIdentifying distributional characteristics in random coefficients panel data models-
dc.typeWorking Paperen
dc.identifier.ppn607538503en
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen
dc.identifier.repecRePEc:ifs:cemmap:22/09en

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