Dynamic conditional score patent count panel data models
Szabolcs Blazsek () and
Alvaro Escribano ()
UC3M Working papers. Economics from Universidad Carlos III de Madrid. Departamento de Economía
We propose a new class of dynamic patent count panel data models that is based on dynamic conditional score (DCS) models. We estimate multiplicative and additive DCS models, MDCS and ADCS respectively, with quasi-ARMA (QARMA) dynamics, and compare them with the finite distributed lag, exponential feedback and linear feedback models. We use a large panel of 4,476 United States (US) firms for period 1979 to 2000. Related to the statistical inference, we discuss the advantages and disadvantages of alternative estimation methods: maximum likelihood estimator (MLE), pooled negative binomial quasi-MLE (QMLE) and generalized method of moments (GMM). For the count panel data models of this paper, the strict exogeneity of explanatory variables assumption of MLE fails and GMM is not feasible. However, interesting results are obtained for pooled negative binomial QMLE. The empirical evidence shows that the new class of MDCS models with QARMA dynamics outperforms all other models considered.
Keywords: Patent; count; panel; data; models; Dynamic; conditional; score; models; Quasi-ARMA; model; Research; and; developmentes; Patent; applications (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-ecm, nep-ino, nep-ipr and nep-pr~
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Persistent link: https://EconPapers.repec.org/RePEc:cte:werepe:we1510
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