A smooth dynamic network model for patent collaboration data - Verena Bauer; Dietmar Harhoff; Göran Kauermann (AStA Advances in Statistical Analysis, 2021)
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.
Keywords: 510; Profile likelihood; Network data; Event data; Patent data; Penalized spline smoothing; Social network analysis