Dear Professor Sven,
Very much thank you! I didn't try negative numbers.
When you said
"I'm also noticing, however, that the parameters of the mean-group
estimator {0.89, -0.46, -1.8} lead us to the (presumably) correct PMG
result again."
Do you mean that after setting
PMG_estimate(&b, , {0.5;0.5;-1},1000)
MG_estimate(&b,1)
I will obtain the MG output corresponding to the automatic setting for the initial values and not the one for {0.5;0.5;-1}?
I would like to add that this PMG package seems to put in advantage Gretl compared to Eviews and Stata. Eviews doesn't allow for MG and Hausman test for the comparison between PMG and MG. Stata does, but when I used 2 lags with the xtpmg package, all the outputs yielded totally irrational coefficients.
As the outputs of Eviews and Gretl were identical in most cases, I combined them. I may be wrong, but the only limitation of PMG Gretl package is the output limited to report the long run and loading coefficients. I think it would be great to include the short run coefficients in the output but of course, I know time is limited and the developers do a great contributions with this and other packages.
Kind regards Reynaldo
On 1/12/21, 15:48 Sven Schreiber <svetosch@gmx.net> wrote:
Am 12.01.2021 um 18:26 schrieb Reynaldo Senra:
> PMG_estimate(&b, , {0;0;0},1000)
> Most of the time, I achieve the exact results of Eviews by setting {1;0;0}.
> Unfortunately, in one case it has been impossible and I really tried
> several combinations. Here I just attached a csv file with the data
> involved in the problem. I also attached the Eviews output and the Gretl
> sample script.
Hi Reynaldo, that's an interesting case study for the PMG package. First
let me note that the automatic setting for the initial values yields a
better likelihood than either {0,0,0} or {1,0,0} in this case.
Anyway, if you try:
PMG_estimate(&b, , {0.5,0.5,-1}, 1000)
then you will get the same results as Eviews. But that's of course
cheating, because I picked those values in the neighborhood of the
results I saw. So that means that in general we're not clever enough
choosing our initial values, and here we apparently got stuck in a local
maximum.
I'm also noticing, however, that the parameters of the mean-group
estimator {0.89, -0.46, -1.8} lead us to the (presumably) correct PMG
result again. Maybe we should routinely also try MG as an
initialization. Jack?
cheers
sven
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