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dc.rights.licensehttp://rightsstatements.org/page/InC/1.0/?language=eses_AR
dc.contributor.authorGaliani, Sebastiánes_AR
dc.contributor.authorGonzález-Rozada, Martínes_AR
dc.date.accessioned2018-03-30T20:53:23Z
dc.date.available2018-03-30T20:53:23Z
dc.date.issued2002-12-27
dc.identifier.urihttp://repositorio.utdt.edu/handle/utdt/10758
dc.description.abstractWe study the finite sample properties of the most important methods of estimation of dynamic panel data models in a special class of small samples: a two-sided small sample (i.e., a sample in which the time dimension is not that short but the cross-section dimension is not that large). This case is encountered increasingly in applied work. Our main results are the following: the estimator proposed by Kiviet (1995) outperforms all other estimators con- sidered in the literature. However, standard statistical inference is not valid for any of them. Thus, to assess the true sample variability of the parameter estimates, bootstrap standard er- rors have to be computed. We find that standard bootstrapping techniques work well except when the autoregressive parameter is close to one. In this last case, the best available solution is to estimate standard errors by means of the Grid-t bootstrap estimator due to Hansen (1999).es_AR
dc.description.sponsorshipPara cualquier uso del contenido del presente documento debe ponerse en contacto con el autor.es_AR
dc.format.extent46 p.es_AR
dc.format.mediumapplication/pdfes_AR
dc.languageenges_AR
dc.publisherUniversidad Torcuato Di Tella. Escuela de Negocios. Centro de Investigaciones en Finanzas (CIF)es_AR
dc.relation.ispartofCentro de Investigaciones Financieras (CIF). Documentos de trabajo 02/2002
dc.rightsinfo:eu-repo/semantics/restrictedAccesses_AR
dc.subjectInferencia estadísticaes_AR
dc.subjectMuestreoes_AR
dc.subjectAnálisis financieroes_AR
dc.titleInference and estimation in small sample dynamic panel data modelses_AR
dc.typeinfo:eu-repo/semantics/workingPaper
dc.type.versioninfo:eu-repo/semantics/acceptedVersiones_AR


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