I also like row wise a little better mainly because it is more logical
to me if we are to add or omit rows depending on the type of
estimation or the data set.
Cheers
Talha
On Sun, Nov 23, 2008 at 6:15 PM, Allin Cottrell <cottrell(a)wfu.edu> wrote:
On Sun, 23 Nov 2008, Riccardo (Jack) Lucchetti wrote:
> On Sat, 22 Nov 2008, Allin Cottrell wrote:
>
> >> So, could the following (Allin's proposal with the adj. R^2 and the
> >> F-statistic swapped) be a reasonable compromise?
> >>
> >> Sum squared resid 59.23226 Mean dependent var 2.962500
> >> S.E. of regression 0.473671 S.D. dependent var 0.615864
> >> R-squared 0.419454 Adjusted R-squared 0.408459
> >> F(5, 264) 38.14888 P-value(F) 2.14e-29
> >> Log-likelihood -178.3244 Akaike criterion 368.6488
> >> Schwarz criterion 390.2393 Hannan-Quinn 377.3186
> >> rho 0.435670 Durbin-Watson 2.036421
> >
> > I like it!
>
> Anyone who doesn't? Speak now or forever hold your peace.
Well... third thoughts! There are quite a few contexts in which
we print somewhat modified regression statistics (for example, for
WLS or AR1 estimation). I presume it's desirable that we handle
as many such cases as we reasonably can in a manner consistent
with plain OLS results. And in that regard it's easiest if we go
all the way with your row-wise presentation. For instance, it's
handy to be able to skip a single row containing the mean and s.d.
of the dependent variable. So here's an example of what I'm
working with at present:
Mean dependent var 4.538837 S.D. dependent var 0.243346
Sum squared resid 1.617235 S.E. of regression 0.189575
R-squared 0.430985 Adjusted R-squared 0.405696
F(2, 45) 17.04203 P-value(F) 3.09e-06
Log-likelihood 13.26255 Akaike criterion -20.52509
Schwarz criterion -14.91149 Hannan-Quinn -18.40371
rho 0.018627 Durbin-Watson 1.960299
(with the last row omitted for non-time series).
Allin.
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