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Score-driven dynamic patent count panel data models

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dc.affiliation.dptoUC3M. Departamento de Economíaes
dc.contributor.authorBlazsek, Szabolcs
dc.contributor.authorEscribano, Álvaro
dc.contributor.editorUniversidad Carlos III de Madrid. Departamento de Economíaes
dc.contributor.otheres
dc.date.accessioned2016-08-02T08:16:40Z
dc.date.available2016-08-02T08:16:40Z
dc.date.issued2016-07
dc.description.abstractThis paper suggests new Dynamic Conditional Score (DCS) count panel data models. We compare the statistical performance of static model, finite distributed lag model, exponential feedback model and different DCS count panel data models. For DCS we consider random walk and quasi-autoregressive formulations of dynamics. We use panel data for a large cross section of United States firms for period 1979 to 2000. We estimate models by using the Poisson quasi-maximum likelihood estimator with fixed effects. The estimation results and diagnostics tests suggest that the statistical performance of DCS-QAR is superior to that of alternative models.en
dc.format.mimetypeapplication/pdf
dc.identifier.issn2340-5031es
dc.identifier.urihttps://hdl.handle.net/10016/23458
dc.identifier.uxxiDT/0000001478es
dc.language.isoenges
dc.relation.hasversionhttp://hdl.handle.net/10016/25168
dc.relation.ispartofseriesUC3M working papers. Economicsen
dc.relation.ispartofseries16-10es
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.accessRightsopen accessen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subject.jelC33es
dc.subject.jelC35es
dc.subject.jelC51es
dc.subject.jelC52es
dc.subject.jelO3es
dc.subject.otherresearch and developmenten
dc.subject.othercount panel dataen
dc.subject.otherdynamic conditional scoreen
dc.subject.otherquasi-maximum likelihooden
dc.titleScore-driven dynamic patent count panel data modelsen
dc.typeworking paper*
dc.type.hasVersionAO*
dspace.entity.typePublication
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