[Under classical assumptions, characterizations are given for two classes of instrumental variable estimators of an equation in a simultaneous system. IV estimators where all instruments are nonstochastic are expressed in terms of multinormal random vectors in exactly the same way as the 2SLS estimator of a just-identified equation. These estimators have no finite moments of positive integral order. The second class, consisting of IV estimators based on certain stochastic instruments, includes the OLS, 2SLS, and modified 2SLS estimators. The inadmissibility (under squared-error loss) of some estimators in this class is considered when the equation being estimated contains two endogenous variables.]

Roberto S. Mariano, Approximations to the Distribution Functions of the Ordinary Least-Squares and Two-Stage Least-Squares Estimators in the Case of Two Included Endogenous Variables, Econometrica, Vol. 41, No. 1 (Jan., 1973), pp. 67-77

[This paper deals with two single-equation estimators in a set of simultaneous linear stochastic equations--namely, ordinary least squares (OLS) and two-stage least squares (2SLS). Under the assumption that all predetermined variables in the model are exogenous, necessary and sufficient conditions are obtained for the existence of even moments of the above estimators. It is shown that for the general case with an arbitrary number of included endogenous variables, even moments of the 2SLS estimator are finite if and only if the order is less than K2 - G1 + 1. Furthermore, even moments of the OLS estimator exist if and only if the order is less than N - K1 - G1 + 1, where N is the sample size, G1 + 1 is the number of included endogenous variables, K1 and K2 respectively are the number of included and excluded exogenous variables in the equation to be estimated.]