Score-driven dynamic patent count panel data models

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dc.contributor.author Blazsek, Szabolcs Istvan
dc.contributor.author Escribano Sáez, Álvaro
dc.contributor.editor Universidad Carlos III de Madrid. Departamento de Economía
dc.contributor.other
dc.date.accessioned 2016-08-02T08:16:40Z
dc.date.available 2016-08-02T08:16:40Z
dc.date.issued 2016-07
dc.identifier.issn 2340-5031
dc.identifier.uri http://hdl.handle.net/10016/23458
dc.description.abstract This 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.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.relation.ispartofseries UC3M working papers. Economics
dc.relation.ispartofseries 16-10
dc.relation.hasversion http://hdl.handle.net/10016/25168
dc.rights Atribución-NoComercial-SinDerivadas 3.0 España
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.other research and development
dc.subject.other count panel data
dc.subject.other dynamic conditional score
dc.subject.other quasi-maximum likelihood
dc.title Score-driven dynamic patent count panel data models
dc.type workingPaper
dc.subject.jel C33
dc.subject.jel C35
dc.subject.jel C51
dc.subject.jel C52
dc.subject.jel O3
dc.rights.accessRights openAccess
dc.type.version draft
dc.identifier.uxxi DT/0000001478
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