Re: [Gretl-devel] basic Julia support
On Sat, 16 Jan 2016, Riccardo (Jack) Lucchetti wrote:
On Sat, 16 Jan 2016, Sven Schreiber wrote:
> Am 15.01.2016 um 20:39 schrieb Allin Cottrell:
>> Following up Jack's comment at
>>
>> http://lists.wfu.edu/pipermail/gretl-devel/2016-January/006467.html
>>
>> in current git there's a basic "preview" of Julia support in
gretl.
>
> exciting!
>
>
>> # NIST's certified coefficient values
>> matrix nist_b = {-3482258.63459582, 15.0618722713733,
>> -0.358191792925910E-01, -2.02022980381683,
>> -1.03322686717359, -0.511041056535807E-01,
>> 1829.15146461355}'
>>
>
> Since I don't have it installed yet, could you comment on whether the
> results match (between gretl/Julia/NIST)?
These are the results I get
<output>
Log-relative errors, Longley coefficients:
gretl julia
12.228 8.0224
10.920 7.5300
11.797 7.5697
12.528 8.1421
13.169 8.3801
11.770 7.2368
12.235 8.0333
Column means
12.092 7.8449
</output>
So it would seem that the MultivariateStats julia module leaves a
bit to be desired for the moment, at lest in terms of precision.
My test was admittedly kinda silly, in that there's not really any
reason to delegate to a "foreign" program stuff that gretl handles
well natively. One would be more likely to get Julia to do MCMC or
the like.
That said, among the various "foreign" programs on which I've tried
the notorious Longley exercise, only numpy comes close to gretl for
numerical precision. However, Anders makes a fair point in saying
that the statistical error (and I would add, data error) swamps the
numerical error for this sort of linear problem.
Allin