English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Paper

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

MPS-Authors
/persons/resource/persons130171

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

Fulltext (public)
There are no public fulltexts available
Supplementary Material (public)
There is no public supplementary material available
Citation

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


Cite as: http://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.