Robust Inference for Dyadic Data with Dependent Ordered Nodes (opens in new tab)

Dyadic regression models are commonly analyzed under the conventional dyadic dependence paradigm, in which two observations may be dependent only if the corresponding dyads share a node. This paper studies inference when this paradigm breaks down because nodes are ordered and nearby nodes are exposed to common latent shocks. In this setting, dyads with no common endpoint may still be dependent when their endpoints are close in the ordering. Al...