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Knowledge spillovers in U.S. patents: a dynamic patent intensity model with secret common innovation factors

Blazsek, Szabolcs; Escribano, Álvaro
Fonte: Universidade Carlos III de Madrid Publicador: Universidade Carlos III de Madrid
Tipo: Trabalho em Andamento Formato: application/pdf
Publicado em /12/2009 ENG
Relevância na Pesquisa
45.87%
During the past two decades, innovations protected by patents have played a key role in business strategies. This fact enhanced studies of the determinants of patents and the impact of patents on innovation and competitive advantage. Sustaining competitive advantages is as important as creating them. Patents help sustaining competivite advantages by increasing the production cost of competitors, by signaling a better quality of products and by serving as barriers to entry. If patents are rewards for innovation, more R&D should be reflected in more patents applications but this is not the end of the story. There is empirical evidence showing that patents through time are becoming easier to get and more valuable to the firm due to increasing damage awards from infringers. These facts question the constant and static nature of the relationship between R&D and patents. Furthermore, innovation creates important knowledge spillovers due to its imperfect appropriability. Our paper investigates these dynamic effects using U.S. patent data from 1979 to 2000 with alternative model specifications for patent counts. We introduce a general dynamic count panel data model with dynamic observable and unobservable spillovers, which encompasses previous models...

Patents, secret innovations and firm's rate of return : differential effects of the innovation leader

Blazsek, Szabolcs; Escribano, Álvaro
Fonte: Universidade Carlos III de Madrid Publicador: Universidade Carlos III de Madrid
Tipo: info:eu-repo/semantics/draft; info:eu-repo/semantics/workingPaper Formato: application/pdf
Publicado em /01/2012 ENG
Relevância na Pesquisa
46.13%
This paper studies the dynamic interactions and the spillovers that exist among patent application intensity, secret innovation intensity and stock returns of a well-defined technological cluster of firms. We study the differential behavior when there is an Innovation Leader (IL) and the rest of the firms are Innovation Followers (IFs). The leader and the followers of the technological cluster are defined according to their patent innovation activity (stock of knowledge). We use data on stock returns and patent applications of a panel of technologically related firms of the United States (US) economy over the period 1979 to 2000. Most firms of the technological cluster are from the pharmaceutical-products industry. Interaction effects and spillovers are quantified by applying several Panel Vector Autoregressive (PVAR) market value models. Impulse Response Functions (IRFs) and dynamic interaction multipliers of the PVAR models are estimated. Secret patent innovations are estimated by using a recent Poisson-type patent count data model, which includes a set of dynamic latent variables. We show that firms’ stock returns, observable patent intensities and secret patent intensities have significant dynamic interaction effects for technologically related firms. The predictive absorptive capacity of the IL is the highest and this type of absorptive capacity is positively correlated with good firm performance measures. The innovation spillover effects that exist among firms...

Dynamic conditional score patent count panel data models

Blazsek, Szabolcs; Escribano, Álvaro
Fonte: Universidade Carlos III de Madrid Publicador: Universidade Carlos III de Madrid
Tipo: info:eu-repo/semantics/submitedVersion; info:eu-repo/semantics/workingPaper
Publicado em 01/11/2015 ENG
Relevância na Pesquisa
156.23%
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.