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4.4. Robustness 
Thus neither large nor small sample properties under ideal 
conditions provide much guide for the choice of an estimator 
from the limited-information class in the present state of know- 
ledge. This is not entirely the case as regards the robustness 
of such estimators, however. KLEIN and NAKAMURA have 
shown that as a consequence of the stochastic nature of % in 
limited-information maximum likelihood, that estimator is more 
sensitive to multicollinearity than are the other members of the 
k-class (*). In the absence of other criteria, these seem grounds 
for abandoning limited-information maximum likelihood in 
practice in favor of some other limited-information estimator. 
There seem to be no very strong reasons, however, for 
choosing among the limited-information estimators other than 
maximum likelihood. The paper on robustness just mentioned 
indicates that these do not differ among themselves as regards 
this property (*!). Since two-stage least squares is the easiest 
of these estimators to compute and since it does provide a na- 
tural generalization of ordinary least squares in the presence 
of theoretically given normalization rules (2), it seems natural 
to choose it in the present state of our knowledge. 
5. NEAR-CONSISTENCY, BLOCK-RECURSIVE SYSTEMS, AND THE 
CHOICE OF ELIGIBLE INSTRUMENTAL VARIABLES 
5.1. Introduction 
In this section we begin the discussion of the choices of 
predetermined instrumental variables which are available and 
(°°) KLEIN and NAKAMURA [16]. 
(*') See also Fisuer [8] for proof that the same is true as regards sensi- 
tivity to specification error. 
(32) See Cuow [7]. 
61 Fisher - pag. 24