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.date.accessioned 2017-09-04T14:37:10Z
dc.date.available 2018-12-01T23:00:07Z
dc.date.issued 2016-12-01
dc.identifier.bibliographicCitation Blazsek, S., Escribano, A. (2016). Score-driven dynamic patent count panel data models. Economics Letters, v. 149, pp. 116-119.
dc.identifier.issn 0165-1765
dc.identifier.uri http://hdl.handle.net/10016/25168
dc.description.abstract In this paper, we propose the use of Dynamic Conditional Score (DCS) count panel data models. We compare the statistical performance of the static model with different dynamic models: finite distributed lag, exponential feedback and different DCS models. For DCS, we consider random walk or quasi-autoregressive dynamics. We use panel data for a large cross section of United States firms for period 1979-2000, and the Poisson quasi-maximum likelihood estimator with fixed effects. The empirical results suggest that DCS has the best statistical performance. (C) 2016 Elsevier B.V. All rights reserved.
dc.description.sponsorship Szabolcs Blazsek acknowledges financial support from Universidad Carlos III de Madrid (ECO2012-33427) and Universidad Francisco Marroquín. Alvaro Escribano acknowledges financial support from the Spanish Ministry of Economy and Competitiveness ECO2015-68715-R, ECO2016-00105-001, Consolidation Grant #2006/04046/002 and María de Maeztu Grant MDM 2014-0431
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Elsevier
dc.relation.isversionof http://hdl.handle.net/10016/23458
dc.rights © Elsevier
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 Patent count panel data
dc.subject.other Dynamic conditional score
dc.subject.other Quasi-maximum likelihood
dc.subject.other Maximum-likelihood methods
dc.subject.other Poisson counts
dc.subject.other Spillovers
dc.title Score-driven dynamic patent count panel data models
dc.type article
dc.subject.jel C33
dc.subject.jel C35
dc.subject.jel C51
dc.subject.jel C52
dc.subject.jel O3
dc.subject.eciencia Economía
dc.identifier.doi https://doi.org/10.1016/j.econlet.2016.10.026
dc.rights.accessRights openAccess
dc.relation.projectID Gobierno de España. ECO2012-33427
dc.relation.projectID Gobierno de España. ECO2015-68715-R
dc.relation.projectID Gobierno de España. ECO2016-00105-001
dc.relation.projectID Gobierno de España. MDM 2014-0431
dc.type.version acceptedVersion
dc.identifier.publicationfirstpage 116
dc.identifier.publicationlastpage 119
dc.identifier.publicationtitle Economics letters
dc.identifier.publicationvolume 149
dc.identifier.uxxi AR/0000018590
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