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Renewable energy innovations in Europe: a dynamic panel data approach

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  • Nadia Ayari
  • Szabolcs Blazsek
  • Pedro Mendi

Abstract

We investigate the determinants of renewable energy R&D intensity and the impact of renewable energy innovations on firm performance, using several dynamic panel data models. We estimate these models using a large data set of European firms from 19 different countries, with some patenting activity in areas related to renewable energies during the 1987 to 2007 period. Our results confirm our priors on the determinants of the rapid development of renewable energy R&D intensity during the past decades. Additionally, we find evidence that renewable patent intensity has a significant dynamic impact on the stock market value of firms.

Suggested Citation

  • Nadia Ayari & Szabolcs Blazsek & Pedro Mendi, 2012. "Renewable energy innovations in Europe: a dynamic panel data approach," Applied Economics, Taylor & Francis Journals, vol. 44(24), pages 3135-3147, August.
  • Handle: RePEc:taf:applec:44:y:2012:i:24:p:3135-3147
    DOI: 10.1080/00036846.2011.570720
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    References listed on IDEAS

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    1. Binder, Michael & Hsiao, Cheng & Pesaran, M. Hashem, 2005. "Estimation And Inference In Short Panel Vector Autoregressions With Unit Roots And Cointegration," Econometric Theory, Cambridge University Press, vol. 21(04), pages 795-837, August.
    2. Jeffrey M. Wooldridge, 2005. "Simple solutions to the initial conditions problem in dynamic, nonlinear panel data models with unobserved heterogeneity," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(1), pages 39-54.
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    6. repec:fth:harver:1473 is not listed on IDEAS
    7. Nadia Ayari & Szabolcs Blazsek & Pedro Mendi, 2012. "Renewable energy innovations in Europe: a dynamic panel data approach," Applied Economics, Taylor & Francis Journals, vol. 44(24), pages 3135-3147, August.
    8. Margolis, Robert M. & Kammen, Daniel M., 1999. "Evidence of under-investment in energy R&D in the United States and the impact of Federal policy," Energy Policy, Elsevier, vol. 27(10), pages 575-584, October.
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    Cited by:

    1. Nadia Ayari & Szabolcs Blazsek & Pedro Mendi, 2012. "Renewable energy innovations in Europe: a dynamic panel data approach," Applied Economics, Taylor & Francis Journals, vol. 44(24), pages 3135-3147, August.
    2. repec:gam:jsusta:v:9:y:2017:i:9:p:1549-:d:110864 is not listed on IDEAS
    3. repec:eee:tefoso:v:122:y:2017:i:c:p:49-62 is not listed on IDEAS
    4. Kruse, Juergen, 2016. "Innovation in Green Energy Technologies and the Economic Performance of Firms," EWI Working Papers 2016-2, Energiewirtschaftliches Institut an der Universitaet zu Koeln (EWI).
    5. Mai Miyamoto & Kenji Takeuchi, 2018. "Explaining Trade Flows in Renewable Energy Products: The Role of Technological Development," Discussion Papers 1819, Graduate School of Economics, Kobe University.

    More about this item

    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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