Uniform laws of large numbers (ULLNs) consider sums of the form: n −1 Σ t n =1 [q t (z t , θ)-Eq t (z t , θ)], where (z t ) denotes a stochastic data generating process that takes its values in a space Z, θ is an element of the parameter space Θ, and q t : Z×Θ→R. ULLNs provide conditions under which the above sum converges to zero uniformly over the parameter space. The purpose of the present note is to introduce a new generic ULLN. It maintains a set of assumptions that is relatively easy to verify and allows at the same time the analysis of a wide variety of estimators and models of interest in economics

A general linear simultaneous equation system with a multivariate Student t disturbance vector is considered. The normal equations of the corresponding maximum likelihood estimator are used as estimator generating equations to introduce a new class of estimators. Properties of large subclasses of these estimators are determined for disturbance vectors other than the multivariate Student t.