A Smooth Dynamic Network Model for Patent Collaboration Data, arXiv preprint 
1909.00736

Bauer, Verena; Harhoff, Dietmar; Kauermann, Göran

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A Smooth Dynamic Network Model for Patent Collaboration Data, arXiv preprint 1909.00736

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

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Bauer, V., Harhoff, D., & Kauermann, G. (2019). A Smooth Dynamic Network Model for Patent Collaboration Data, arXiv preprint 1909.00736.

Cite as: https://hdl.handle.net/21.11116/0000-0004-A0F8-F

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.