A Smooth Dynamic Network Model for Patent Collaboration Data

Bauer, Verena; Harhoff, Dietmar; Kauermann, Göran

doi:10.1007/s10182-021-00393-w

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A Smooth Dynamic Network Model for Patent Collaboration Data

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Harhoff, Dietmar
MPI for Innovation and Competition, Max Planck Society;

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https://arxiv.org/pdf/1909.00736.pdf
(Preprint)

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Citation

Bauer, V., Harhoff, D., & Kauermann, G. (2022). A Smooth Dynamic Network Model for Patent Collaboration Data. AStA - Advances in Statistical Analysis, 106, 97-116. doi:10.1007/s10182-021-00393-w.

Cite as: https://hdl.handle.net/21.11116/0000-0007-E9A2-B

Abstract

The development and application of models, which take the evolution of networks with a dynamical structure into account are receiving increasing attention. Our research focuses on a profile likelihood approach to model time-stamped event data for a large-scale network applied on patent collaborations. As event we consider the submission of a joint patent and we investigate the driving forces for collaboration between inventors. We propose a flexible semiparametric model, which allows to include covariates built from the network (i.e. collaboration) history.