Publication: Dynamic conditional score patent count panel data models
Loading...
Identifiers
Publication date
2015-11-01
Defense date
Authors
Advisors
Tutors
Journal Title
Journal ISSN
Volume Title
Publisher
Impact
Export
Abstract
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.
Description
Keywords
Patent count panel data models, Dynamic conditional score models, Quasi-ARMA model, Research and developmentes, Patent applications