  Traditional inference in relational event models from dynamic network data
involves only dyadic and node-specific variables, as anything that is global,
i.e. constant across dyads, drops out of the partial likelihood. We address
this with the use of nested case-control sampling on a time-shifted version of
the event process. This leads to a partial likelihood of a degenerate logistic
additive model, enabling efficient estimation of global and non-global
covariate effects. The method's effectiveness is demonstrated through a
simulation study. An application to bike sharing data reveals significant
influences of global covariates like weather and time of day on bike-sharing
dynamics.
