Conventional methods of cluster-robust inference are inconsistent in the presence of
unignorably large clusters. We formalize this claim by establishing a necessary and …

We obtain central limit theorems for data coming from a sparse, small world network, ie a
network with limited maximal degree and a relatively small (but growing) diameter …

The recent literature points out that the conventional cluster-robust standard errors fail in the
presence of large clusters. We propose a novel cluster-robust score subsampling inference …