  The Network Disturbance Model of Doreian (1989) expresses the dependency
between observations taken at the vertices of a network by modelling the
correlation between neighbouring vertices, using a single correlation parameter
$\rho$. It has been observed that estimation of $\rho$ in dense graphs, using
the method of Maximum Likelihood, leads to results that can be both biased and
very unstable. In this paper, we sketch why this is the case, showing that the
variability cannot be avoided, no matter how large the network. We also propose
a more intuitive estimator of $\rho$, which shows little bias. The related
Network Effects Model is briefly discussed.
