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does not become consistent when one changes a current endo- 
genous variable to a recent past value of the same variable even 
if triangularity of the A matrix is achieved in this way. The 
assumptions which lead to the consistency of least squares re- 
quire more than this and all the same difficulties will still be 
encountered even if they go unrecognized. 
2.4. The Proximity Theorem and Near-Consistency 
As already remarked, however, the issue of the use of 
ordinary least squares (or indeed of any particular estimator) 
is not whether the assumptions thereof are precisely satisfied. 
Rather the crucial question is that of how closely they are 
satisfied, of how those assumptions stand up as approximations 
rather than as exact statements. The problem is not a discrete 
one; rather it is continuous. Moreover, the question of good- 
ness of approximation is itself dependent on the sensitivity of 
the properties of the estimator to variation in the assumptions 
thereof. In general, the less sensitive is an estimator, the greater 
the tolerable deviation from the strict conditions under which it 
has desirable properties. 
In the present instance, our discussion has largely run in 
terms of consistency. Consistency, however, is a rather weak, 
although desirable property. Since ordinary least squares has 
several other attractive features, we might plausibly be willing 
to tolerate small inconsistencies to gain, for example, compu- 
tational ease, small variance around probability limits, and so 
forth. Tt is thus not sufficient to ask whether the assumptions 
under which ordinary least squares is consistent are satisfied; 
we must ask whether the fact that they are not generally satis- 
fied in economy-wide econometric models is likely to be of 
much Importance. 
This question is formally answered by the Proximity Theo- 
‘61 Fisher - pag. 12