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squares will be inconsistent for at least one equation in the
model.
What is less often realized in practice is the role played by
the assumptions on the disturbances. Because of the simplicity
and other advantages of ordinary least squares, there is a natural
 tendency to settle for a triangular A and to overlook the
fact that such triangularity does not suffice to make ordinary
least squares consistent (%).
To see that such assumptions are generally required, consider
 first the assumption that V(o) is diagonal. If this fails,
then (2.5) shows that W(0) cannot generally be taken to be
triangular, whence ordinary least squares will be inconsistent.
This corresponds to the intuitive idea that if a high-numbered
and a low-numbered disturbance are correlated, the endogenous
variable corresponding to the low-numbered disturbance cannot
 be taken to be uncorrelated with the high-numbered disturbance
 even if thre is no direct influence through the explicit
equations of the model. Indeed, not only is the diagonality of
V(o) required for the consistency of ordinary least squares,
but also, if nothing more is known of the coefficients of the
model save that A is triangular, such an assumption is necessary
 for the very identifiability of the equations (°).
It is possible, however, to alter the assumption of no serial
correlation. Clearly, this enters in both (2.5) and (2.6) because
y,_ appears in the model. If this were not the case, the assumption
 in question would not be needed for consistency.
In most econometric models, however, and certainly in economy-wide
 ones, lagged values of the endogenous variables do
in fact appear. We are nevertheless able to weaken the noserial-correlation
 assumption (R.3) to:
(R.3*) B (as well as A) is triangular with zeros above
the diagonal, and for all 0>o, V(0) is triangular with the same
arrangement of zeros so that high-numbered disturbances are

() In fairness, it should be pointed out that Worp’s theoretical writings
are entirely clear on this point. See Worp 36, pp. 358-359], for example.
(®) See F1sHER [10]

‘6] Fisher - pag. 8