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Secondly, it is of course possible that Professor WoLD’s J? can 
be less than 1. This can happen if structural change takes the form 
of only a small change in the parameters but a large downward 
change in the variance of the disturbance terms. 
My next point is that Professor WoLp has been careful to avoid 
an error which is occasionally made in the literature, namely that 
least squares provides an unbiased forecast of the dependent variable. 
That is false unless the least squares parameters are themselves un- 
biased and this is not the case if, for example, a lagged dependent 
variable appears on the right-hand side of the equation. Professor 
Worp, however, has not said this although he has said something 
which sounds like it. What he has said is that least squares is a 
consistent estimator of an unbiased predictor and this, as he has 
shown, is true. 
Finally, it seems to me that Professor Wozp is unduly worried 
about what he calls the danger of over-fitting in the reduced form. 
The circumstance in which the reduced form cannot be estimated 
by ordinary least squares because there are too few observations 
relative to the number of exogenous variables in the model, is quite 
a common one in dealing with large econometric models. This is not 
of great consequence as a fundamental matter, however, because 
one then uses intrumental variables methods which drop some of 
the exogenous variables for purposes of estimation. No difficulty 
of principle arises, although there is then a problem of how one 
ought to choose the instrumental variables to be retained. This 
is a question which I cover in my paper. 
[HEIL 
I. Regarding the difficulty of over-fitting in the reduced form, 
Messrs. T. KLoEK and L.B.M. MENNES formulated a procedure (in 
a recent issue of « Econometrica ») which is designed to handle 
this problem, which is indeed serious when the number of pre- 
determined variables is not small compared with the number 
"2] Wold - pag. 54