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A smooth dynamic network model for patent collaboration data

Author

Listed:
  • Verena Bauer

    (Ludwig-Maximilians-Universität)

  • Dietmar Harhoff

    (Max Planck Institute for Innovation and Competition)

  • Göran Kauermann

    (Ludwig-Maximilians-Universität)

Abstract

The development and application of models, which take the evolution of network dynamics into account, are receiving increasing attention. We contribute to this field and focus on a profile likelihood approach to model time-stamped event data for a large-scale dynamic network. We investigate the collaboration of inventors using EU patent data. As event we consider the submission of a joint patent and we explore the driving forces for collaboration between inventors. We propose a flexible semiparametric model, which includes external and internal covariates, where the latter are built from the network history.

Suggested Citation

  • Verena Bauer & Dietmar Harhoff & Göran Kauermann, 2022. "A smooth dynamic network model for patent collaboration data," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 106(1), pages 97-116, March.
    • Handle: RePEc:spr:alstar:v:106:y:2022:i:1:d:10.1007_s10182-021-00393-w
    DOI: 10.1007/s10182-021-00393-w
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    References listed on IDEAS

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