Hi!
I downloaded the latest snapshot of gretl for Windows (build date
2008-11-26) and I realized that the new model presentation layout was
abandoned in the Brazilian Portuguese translation, but not in English.
Best,
Henrique C. de Andrade
Doutorando em Economia Aplicada
Universidade Federal do Rio Grande do Sul
www.ufrgs.br/ppge
On Mon, Nov 24, 2008 at 7:12 AM, Ignacio Diaz-Emparanza
<ignacio.diaz-emparanza(a)ehu.es> wrote:
El Sunday 23 November 2008 19:57:13 Riccardo (Jack) Lucchetti
escribió:
> On Sun, 23 Nov 2008, Sven Schreiber wrote:
> > Am 23.11.2008 17:15, Allin Cottrell schrieb:
> >> 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).
> >
> > Why actually have both the rho and the DW stat? (Assuming rho is the
> > first-order autocorrelation coeff, right?)
>
I also don't see the necessity of rho (well, only for having an even number of
items here). As a former RATS user I miss, in the context of ARIMA models
(not for ols), the Ljung-Box statistic.
>
> What I usually teach my students is that DW has just historical importance
> and that if you want to test for autocorrelation the LM (Godfrey) test is
> a much better tool. So, in principle I agree with you 100%. The only cases
> (that I can think of right now) when I may want to actually see the DW
> statistic are:
>
> 1) as always, for comparison/replication purposes
> 2) in the context of teaching what a spurious regression is (Granger &
> Newbold's famous "rule of thumb")
> 3) in the context of cointegration tests, but IMHO nowadays Sargan and
> Bhargava's idea is quite outdated too.
>
> Hence, I wouldn't miss DW very much personally.
I vote for DW as well.
> > Next, an unrelated suggestion: What do you think about grouping the
> > adjusted R-squared and the info criteria together, since conceptually
> > they are more or less the same thing.
I prefer leaving the things as above. I consider the F() test may be
interpreted as a type of "significance test" for the R^2. Remember that
F=[R^2/(k-1)]/[(1-R^2)/(T-k)]
so I think it is natural that the F test goes inmediately after the R^2.
> Agreed. Once we're at it, I'd like to express my preference for the
"AIC",
> "BIC" and "HQC" acronyms, which shouldn't necessarily be
translated.
I also vote for AIC, BIC and HQC.
--
Ignacio Diaz-Emparanza
DEPARTAMENTO DE ECONOMÍA APLICADA III (ECONOMETRÍA Y ESTADÍSTICA)
UPV/EHU
Avda. Lehendakari Aguirre, 83 | 48015 BILBAO
T.: +34 946013732 | F.: +34 946013754
www.et.bs.ehu.es
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--
Henrique C. de Andrade
Doutorando em Economia Aplicada
Universidade Federal do Rio Grande do Sul
www.ufrgs.br/ppge