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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.

Suggested Citation

  • 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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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Shuo Han & Weijun Cui & Jin Chen & Yu Fu, 2019. "Female CEOs and Corporate Innovation Behaviors—Research on the Regulating Effect of Gender Culture," Sustainability, MDPI, Open Access Journal, vol. 11(3), pages 1-22, January.
    2. Suzuki, Keishun, 2017. "Competition, Patent Protection, and Innovation in an Endogenous Market Structure," MPRA Paper 77133, University Library of Munich, Germany.
    3. Blazsek, Szabolcs & Escribano, Alvaro, 2016. "Score-driven dynamic patent count panel data models," Economics Letters, Elsevier, vol. 149(C), pages 116-119.
    4. 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 Working Papers JRC101847, Joint Research Centre (Seville site).

    More about this item

    Keywords

    Patent propensity; Competition; Innovation leaders; Panel vector autoregression; Simulated maximum likelihood;

    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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