Abstract We extend the well‐known β$$ \beta $$‐model for directed graphs to dynamic network setting, where we observe snapshots of adjacency matrices at different time points. We propose a kernel‐smoothed likelihood approach for estimating 2n$$ 2n $$ time‐varying parameters in a network with n$$ n $$ nodes, from N$$ N $$ snapshots. We establish consistency and asymptotic normality properties of our kernel‐smoothed estimators as either n$$ n $$ or N$$ N $$ diverges. Our results contrast their counterparts in single‐network analyses, where...
