INTRODUCTION THE METHOD OF INSTRUMENTAL VARIABLES (IV), proposed independently by Reiersol [15] and Geary [4], is one of the most useful classes of estimation techniques available to econometricians. The method is particularly useful in the context of errors-in-variables and simultaneous equation problems, and it ineludes ordinary least squares (OLS), two-stage least squares (2SLS) (Klein [9]), three-stage least squares (3SLS) (Madansky [12]) and certain full-information maximum likelihood estimators (Hausman [7]) as special cases. To date, the definitive theoretical treatment of the instrumental variables method is that of Sargan [16]. Important contributions have also been made by Brundy and Jorgenson [!, 2]. However, this work has not established the properties of IV estimators for all of the kinds of data analyzed by economists. Sargan's work is appropriate for data which are stochastic processes of the kind considered by Koopmans, Rubin, and Leipnik 10] in their study of dynami

[This paper examines the consequences and detection of model misspecification when using maximum likelihood techniques for estimation and inference. The quasi-maximum likelihood estimator (OMLE) converges to a well defined limit, and may or may not be consistent for particular parameters of interest. Standard tests (Wald, Lagrange Multiplier, or Likelihood Ratio) are invalid in the presence of misspecification, but more general statistics are given which allow inferences to be drawn robustly. The properties of the QMLE and the information matrix are exploited to yield several useful tests for model misspecification.]