  This paper studies inference in models of discrete choice with social
interactions when the data consists of a single large network. We provide
theoretical justification for the use of spatial and network HAC variance
estimators in applied work, the latter constructed by using network path
distance in place of spatial distance. Toward this end, we prove new central
limit theorems for network moments in a large class of social interactions
models. The results are applicable to discrete games on networks and dynamic
models where social interactions enter through lagged dependent variables. We
illustrate our results in an empirical application and simulation study.
