  In this paper we use a censored regression model to investigate data on the
international trade of small arms and ammunition (SAA) provided by the
Norwegian Initiative on Small Arms Transfers (NISAT). Taking a network based
view on the transfers, we not only rely on exogenous covariates but also
estimate endogenous network effects. We apply a spatial autocorrelation (SAR)
model with multiple weight matrices. The likelihood is maximized employing the
Monte Carlo Expectation Maximization (MCEM) algorithm. Our approach reveals
strong and stable endogenous network effects. Furthermore, we find evidence for
a substantial path dependence as well as a close connection between exports of
civilian and military small arms. The model is then used in a "forensic" manner
to analyse latent network structures and thereby to identify countries with
higher or lower tendency to export or import than reflected in the data. The
approach is also validated using a simulation study.
