Before revision:  We also propose a heteroskedasticity-autocorrelation consistent variance estimator and prove its consistencyunder regularity conditions. The finite sample performance of this latter estimator is investigated through a Monte Carlo simulation study .
After revision:  We also provide a method of calculating standard errors robust to general forms of network dependence. For that purpose, we rely on a network heteroskedasticity and autocorrelation consistent (HAC) variance estimator, and show its consistency. The results rely on conditions characterized by tradeoffs between the rate of decay of dependence across a network and network's denseness. Our approach can accommodate data generated by network formation models, random fields on graphs, conditional dependency graphs, and large functional-causal systems of equations .