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

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, is able to control for the endogeneity of R&D and therefore can be consistently estimated by maximum likelihood. Apart from allowing for firm specific fixed and random effects, we introduce a common unobserved component, or secret stock of knowledge, that affects differently the propensity to patent of each firm across sectors due to their different absorptive capacity.Point process, Conditional intensity, Latent factor, R&D spillovers, Patents, Secret innovations

Regime switching models of hedge fund returns

We estimate and compare the forecasting performance of several dynamic models of returns of different hedge fund strategies. The conditional mean of return is an ARMA process while its conditional volatility is modeled according to the GARCH specification. In order to take into account the high level of risk of these strategies, we also consider a Markov switching structure of the parameters in both equations to capture jumps. Finally, the one-step-ahead out-of-sample forecast performance of different models is compared.Markov switching ARMA-GARCH, forecasting performance

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

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, due to the imperfect appropriability of the returns of the investment in R&D, are specially important for secret innovations and less relevant for observed innovations. The flow of spillovers between followers and the leader is not symmetric being higher from the IL to the IFs.Patent count data model, Stock market value, Secret innovations, Absorptive capacity, Technological proximity, Panel Vector Autoregression (PVAR), Impulse Response Function (IRF), Efficient Importance Sampling (EIS)

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

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, due to the imperfect appropriability of the returns of the investment in R&D, are specially important for secret innovations and less relevant for observed innovations. The flow of spillovers between followers and the leader is not symmetric being higher from the IL to the IFs

Propensity to patent, R&D and market competition : dynamic spillovers of innovation leaders and followers

Dynamic interactions among stock return, Research and Development (R&D) expenses, patent applications based on R&D investment, and the propensity to patent are studied in this work for a panel of firms from the United States. The panel includes technologically similar firms, neck-to-neck, mostly from the drugs product-market sector. Firms’ propensity to patent is modeled by a dynamic latent-factor patent count data model that separates patented and non patented R&D. Patent innovation leader and follower firms are identified according to their knowledge stock. Significant and positive dynamic spillover effects are obtained among patent application leaders and followers. We observe that neck-to-neck firms in patent innovation activity produce an inverted-U relationship between market competition and innovation. Furthermore, firms’ propensity to patent is positively correlated with market competition and there is a positive feedback in both directions. Increasing the degree of competition in the market enhances innovation and patent applications, in order to help firms to appropriate part of the benefits of their R&D investments. On the other hand, firms by increasing their patent applications defend themselves from competitors, trying to improve their market share. However, due to the diffusion of knowledge through patent applications, knowledge spills over to competitors therefore, the degree of competition and innovation increases in the market

Dynamic conditional score patent count panel data models

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

Score-driven dynamic patent count panel data models

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

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

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, is able to control for the endogeneity of R&D and therefore can be consistently estimated by maximum likelihood. Apart from allowing for firm specific fixed and random effects, we introduce a common unobserved component, or secret stock of knowledge, that affects differently the propensity to patent of each firm across sectors due to their different absorptive capacity

Patent propensity, R&D and market competition: Dynamic spillovers of innovation leaders and followers

In this article, dynamic interactions among stock return, Research and Development (R&D) investment, patent applications and patent propensity of firms are studied. Patent innovation leader and follower firms are identified with respect to their quality-adjusted knowledge stock. Significant and positive dynamic spillover effects are obtained in a panel vector autoregressive model. We find positive dynamic spillover effects from patent innovation leader to followers. We show that an increasing degree of competition enhances innovation and patent applications, which helps firms appropriating part of the benefits of their R&D investments. (C) 2015 Elsevier B.V. All rights reserved.The first author acknowledges and gives thanks for research funding from the School of Business of Universidad Francisco Marroquín, Guatemala. Funding from the Bank of Spain Excellence Program and the Ministry of Economy and Competitiveness of Spain (research project ECO2012-33427 and grant MDM2014-0431) is gratefully acknowledged by the second author

Co-integration with score-driven models: an application to US real GDP growth, US inflation rate, and effective federal funds rate

Nonlinear co-integration is studied for score-driven models, using a new multivariate dynamic conditional score/generalized autoregressive score model. The model is named t-QVARMA (quasi-vector autoregressive moving average model), which is a location model for the multivariate t-distribution. In t-QVARMA, I(0) and co-integrated I(1) components of the dependent variables are included. For t-QVARMA, the conditions of the maximum likelihood estimator and impulse response functions (IRFs) are presented. A limiting special case of t-QVARMA, named Gaussian-QVARMA, is a Gaussian-VARMA specification with I(0) and I(1) components. As an empirical application, the US real gross domestic product growth, US inflation rate, and effective federal funds rate are studied for the period of 1954 Q3 to 2020 Q2. Statistical performance and predictive accuracy of t-QVARMA are superior to those of Gaussian-VAR. Estimates of the short-run IRF, long-run IRF, and total IRF impacts for the US data are reported.The paper was presented at the GESG Research Seminar of Universidad Francisco Marroquín (June 20, 2019, Guatemala City). The authors greatly appreciate the comments of the anonymous reviewers of the journal, Juan Carlos Arriaza Herrera, Astrid Ayala, Matthew Copley, Carla Hess, and Søren Johansen. All remaining errors are our own. No potential conflict of interest is reported by the authors. Computer codes are available from the authors upon request. Blazsek and Licht acknowledge funding from Universidad Francisco Marroquín. Escribano acknowledges funding from Ministerio de Economía, Industria y Competitividad (ECO2016-00105-001 and MDM 2014-0431), Comunidad de Madrid (MadEco-CM S2015/HUM-3444), and Agencia Estatal de Investigación (2019/00419/001)