Chapter 25 (Model Selection Criteria) in the Gretl user's guide defines the
AIC as
AIC = -2‘(θ ˆ) + 2k - *(Pasted formula not quite correct - see Guide ) *
 The formulas that you quote are both monotonic transformations of this
formula.  That is the order of the models is not changed under these
transformations.  All formulas lead to the same conclusion.  I would
presume that some formulas are easier to calculate for certain types of
models.
I would also comment that estimating 10 coefficients and  co-variances
with 10  observations (and 2 obs to initialize) will not lead to reliable
results.
John C Frain
3 Aranleigh Park
Rathfarnham
Dublin 14
Ireland
www.tcd.ie/Economics/staff/frainj/home.html
mailto:frainj@tcd.ie
mailto:frainj@gmail.com
On 2 March 2018 at 12:21, Sven Schreiber <svetosch(a)gmx.net> wrote:
 Am 02.03.2018 um 12:36 schrieb Marvin Berndt:
> Dear Ioannis,
> the residual in the Excel-sheets are those from the VAR regresison. I
> extracted them from the VAR regression Output via '/Save --> Residuals from
> equation 1/2/'. I used those residuals in order to try to recreate the AIC
> in gretl.
>
 With such a short sample I suspect it makes a big difference whether 1/T
 or 1/(T-K) is used for the (co)variance calculation. Both variants are
 "correct" in that the method is for large samples anyway.
 There was a discussion on this (ore the devel) mailing list about whether
 to apply a d.o.f.-correction for VARs but I cannot find it. I agree it
 could be documented better, the VAR chapter in the guide is --alas-- still
 not finished.
 I _believe_, however, that gretl for VARs does it without this
 small-sample correction (this would also apply to the $sigma accessor, to
 those who know what that is).
 In principle we could all look it up in the source, right? Hehe. (Apart
 from the fact that currently sourceforge is not accessible.)
 cheers,
 sven
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