RT Generic
T1 Dynamic conditional score patent count panel data models
A1 Blazsek, Szabolcs
A1 Escribano, Álvaro
A2 Universidad Carlos III de Madrid. Departamento de Economía, 
AB We propose a new class of dynamic patent count panel data models that is based on dynamicconditional score (DCS) models. We estimate multiplicative and additive DCS models, MDCS and ADCSrespectively, 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 anddisadvantages of alternative estimation methods: maximum likelihood estimator (MLE), poolednegative binomial quasi-MLE (QMLE) and generalized method of moments (GMM). For the countpanel data models of this paper, the strict exogeneity of explanatory variables assumption of MLE failsand GMM is not feasible. However, interesting results are obtained for pooled negative binomialQMLE. The empirical evidence shows that the new class of MDCS models with QARMA dynamicsoutperforms all other models considered.
SN 2340-5031
YR 2015
FD 2015-11-01
LK https://hdl.handle.net/10016/22040
UL https://hdl.handle.net/10016/22040
LA eng
DS e-Archivo
RD 28 may. 2024