Score-driven dynamic patent count panel data models
Szabolcs Blazsek () and
Alvaro Escribano ()
UC3M Working papers. Economics from Universidad Carlos III de Madrid. Departamento de Economía
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.
Keywords: research; and; development; count; panel; data; dynamic; conditional; score; quasi-maximum; likelihood (search for similar items in EconPapers)
JEL-codes: C33 C35 C51 C52 O3 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-bec, nep-ecm, nep-ino and nep-ipr
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Journal Article: Score-driven dynamic patent count panel data models (2016)
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Persistent link: https://EconPapers.repec.org/RePEc:cte:werepe:23458
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