Allin Cottrell schrieb:
On Thu, 6 Dec 2007, Sven Schreiber wrote:
>> On the original issue of R^2, I'll file the bug report soon.
>>
>>
> "Delay that order"... Actually for my real-world cases it turns
> out there isn't anything (obviously) wrong. However, I'm still
> puzzled by the 3-liner results I posted earlier. The point
> estimates are quite different between ols and tsls -- then how
> come the correlation between fitted and observed is the same to
> five or six digits of precision? Hm.
>
"Strange but true". It seems to be in the arithmetic for the case
of one independent variable and one instrument.
nulldata 50
genr x = normal()
genr y = normal()
genr z = normal()
ols y 0 x
ols x 0 z --quiet
genr xhat = $coeff(const) + $coeff(z)*z
ols y 0 xhat
genr yhat = $coeff(const) + $coeff(xhat)*x
R2 = corr(y, yhat)^2
"R2" is numerically identical to the R^2 from the first OLS.
Left as an exercise: prove that this is always the case.
Sorry to leave that unclarified, Jack already pointed out the (simple)
solution of this pseudo-mystery directly to me. (Trying to not embarrass
me publicly I guess...) Any linear transformation (or some may call it
affine) of a single variable will leave the linear correlation with
another variable unchanged of course. So the estimated coefficients are
really totally irrelevant in this case.
cheers,
sven