MLE Advanced
by Mario Florez Porras
Hello,dear Gretl Community.
I'm doing a nonlinear estimation via the maximum likelihood method. I want
to do a batch processing of data but in some cases the estimation don't
satisfise tolerance (1,81899e-012), don't met the convergence criterion.
Does anyone knows how it is calculated whether or not the model meets
tolerance?
An estimation that don't met tolerance;
[image: Imágenes integradas 1]
An estimation that met tolerance;
[image: Imágenes integradas 2]
[image: Imágenes integradas 3]
Suggestion: I think it's calculated by adding up logslikelihood or
gradients. But I do both and don't coincided with tolerance.
Another question, How can I save automatically results?
Beforehand, thank you for any help.
Mario Alberto Flórez Porras
Student of Industrial Engineering
10 years, 1 month
- 5
- 5
Panel plot in a loop
by cociuba mihai
I'm interested to know if there are any possibilities to generate panel
plots from a script, or to retrieve the mean of the selected series.
Also the panel plot command doesn't appear in the command log.
<HANSEL>
open abdata.gdt
summary
#there are some missing observation
smpl full
smpl YEAR>1978 --restrict
smpl YEAR<1983 --restrict
summary
#no missing observation
# for every industry in which the company operates do a panel plot for wage
#pseudo-code
loop i=1..8
smpl full
smpl IND=$i --restrict
summary WAGE --simple
# select WAGE, right click select panel plot/group means
#is it posibile to generate a panel plot in a loop
# or at least to retrive the information wich is used ( the mean of the
series for every year???
endloop
<HANSEL>
Best,
Mihai
10 years, 1 month
- 3
- 2
MLE Advanced
by Ether
From: Mario Florez Porras <mario.florez.porras(a)gmail.com>
To: gretl-users(a)lists.wfu.edu
Sent: Tuesday, August 30, 2016 4:36 PM
Subject: [Gretl-users] MLE Advanced
Anyone know how it is calculated the covariance matrix?
Here is Octave code which produces the same covariance values as gretl:
degrees_of_freedom = cases-predictorscoefficients = pinv(IV)*DV
estimates = IV*coefficients;
residuals = DV-estimates;
SSres = residuals'*residuals;
residual_variance = SSres/degrees_of_freedom; N=IV'*IV;
Ni=inv(N);
covariance_matrix = Ni*residual_variance;
10 years, 1 month
- 1
- 0