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

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  • Blazsek, Szabolcs
  • Escribano, Alvaro

Abstract

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.

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  • Blazsek, Szabolcs & Escribano, Alvaro, 2016. "Patent propensity, R&D and market competition: Dynamic spillovers of innovation leaders and followers," Journal of Econometrics, Elsevier, vol. 191(1), pages 145-163.
    • Handle: RePEc:eee:econom:v:191:y:2016:i:1:p:145-163
    DOI: 10.1016/j.jeconom.2015.10.005
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    3. Suzuki, Keishun, 2017. "Competition, Patent Protection, and Innovation in an Endogenous Market Structure," MPRA Paper 77133, University Library of Munich, Germany.
    4. Blazsek, Szabolcs & Escribano, Alvaro, 2016. "Score-driven dynamic patent count panel data models," Economics Letters, Elsevier, vol. 149(C), pages 116-119.
    5. AIVAZ Kamer-Ainur & TOFAN Ionela, 2022. "The Synergy Between Digitalization And The Level Of Research And Business Development Allocations At Eu Level," Studies in Business and Economics, Lucian Blaga University of Sibiu, Faculty of Economic Sciences, vol. 17(3), pages 5-17, December.
    6. Sisi Zheng & Shanyue Jin, 2023. "Can Enterprises in China Achieve Sustainable Development through Green Investment?," IJERPH, MDPI, vol. 20(3), pages 1-25, January.
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    8. Xin Sheng & Wenya Chen & Decai Tang & Bright Obuobi, 2023. "Impact of Digital Finance on Manufacturing Technology Innovation: Fixed-Effects and Panel-Threshold Approaches," Sustainability, MDPI, vol. 15(14), pages 1-24, July.
    9. Michaela Kotkova Striteska & Viktor Prokop, 2020. "Dynamic Innovation Strategy Model in Practice of Innovation Leaders and Followers in CEE Countries—A Prerequisite for Building Innovative Ecosystems," Sustainability, MDPI, vol. 12(9), pages 1-20, May.
    10. Sara Alonso-Muñoz & Eva Pelechano-Barahona & Rocío González-Sánchez, 2020. "Participation in Group Companies as a Source of External Knowledge in Obtaining and Making Profitable Radical Innovations," Sustainability, MDPI, vol. 12(18), pages 1-19, September.
    11. Yuanyuan Dong & Zepeng Wei & Tiansen Liu & Xinpeng Xing, 2020. "The Impact of R&D Intensity on the Innovation Performance of Artificial Intelligence Enterprises-Based on the Moderating Effect of Patent Portfolio," Sustainability, MDPI, vol. 13(1), pages 1-17, December.
    12. Chen, Chao & Gu, Junjian & Luo, Rongxi, 2022. "Corporate innovation and R&D expenditure disclosures," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    13. Nestor Duch-Brown & Andrea de Panizza & Ibrahim Kholilul Rohman, 2016. "Innovation and productivity in a S&T intensive sector: the case of Information industries in Spain," JRC Research Reports JRC101847, Joint Research Centre.

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    More about this item

    Keywords

    Patent propensity; Competition; Innovation leaders; Panel vector autoregression; Simulated maximum likelihood;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C41 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Duration Analysis; Optimal Timing Strategies

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