Re: panplot question
by Sven Schreiber
Am 27.01.2023 um 15:05 schrieb Summers, Peter:
>
> Hi folks,
>
> I’m creating a couple graphs using panplot, and I’d like to have the
> x-axis labels be the date (year), rather than the time period number.
> Is there a way to do that? (I realize that may be a gnuplot question.)
> More generally, would it be possible to incorporate panplot
> functionality into the plot…end plot environment?
>
Peter, what is the time periodicity of your panel dataset, annual? Can
you perhaps tell us which of the shipped example files in gretl are
closest to your situation?
thanks
sven
3 years, 2 months
panplot question
by Summers, Peter
Hi folks,
I'm creating a couple graphs using panplot, and I'd like to have the x-axis labels be the date (year), rather than the time period number. Is there a way to do that? (I realize that may be a gnuplot question.) More generally, would it be possible to incorporate panplot functionality into the plot...end plot environment?
Thanks,
Peter
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3 years, 2 months
BACE Estimation difficulties
by Brian Revell
I thought i would try this option estimating a simple model (always the
best way to gain experience and confidence with the routine).
Independent variable, 1 indep variable . No lag on Y. . Zero selected for
out of sample forecast Constant -can be omitted. Best 4 models selected
-again as the default.
This was the output:-
Dependent viariable was removed from the X list.* It is not there -there is
only a single indep variable. *
Error message from BACE_GUI():
Average model size should be greater then 0 and lower than K !!! *Average
model size specified is the default 1*
*Some guidance wlecomed please*
*Brian*
3 years, 2 months
BACE GUI Output
by Brian Revell
Just a minor point. It would be helpful if the sample size or sample time
period was displayed in BACE outputfrom the GUI.. No need to print it out
with each model iteration -with the initial OLS model would suffice.
Brian
3 years, 3 months
Combined irf in VAR/VEC model.
by Olasehinde Timmy
Dear Admin,
I am trying to plot the impulse response(irf) after estimating a 5-variable
VAR/VEC model, however, I realized that the combined irf graphics option
disappeared. But when I reduced the variables to four, it reappeared.
Was this a bug or something? Then I would like to know how to go about it.
Regards
Timmy.
3 years, 3 months
Re: How to apply the HAC option in Gretl?
by Cottrell, Allin
On Mon, Nov 7, 2022 at 12:29 PM Torbjørn Lorentzen
<torbjorn.lorentzen(a)outlook.com> wrote:
>
> I'm a new user of Gretl. I'm using ols in the estimation of a time series model. T want to us ethe HAC-option to correct for autocorrelation and heteroscedasticity. I tried the robust option, but to what I can see it only corrects for heteroscedasticity? Great if anyone could help me with the Gretl script for HAC and how to put it together with the ols command. All suggestions are appreciated 😊
Hi Tobben. If you go to the menu item /Tools/Preferences/General
you'll see an "HCCME" tab. That lets you set your preferences for
robust covariance matrix estimation. Note that the HAC option is only
available when your dataset is recognized as time-series.
In scripting usage the relevant option is --robust. What exactly that
does depends on the aforementioned setting. But if the data are time
series gretl will use the HAC estimator by default. In that case you
should see a line like the following in the estimation output:
HAC standard errors, bandwidth 4 (Bartlett kernel)
Allin Cottrell
3 years, 3 months
Some suggestions on the "Number of cases ’correctly predicted'" from a logit/probit outputs
by Fred Engst
Hi Jack, Allin, Sven and all others on the gretl team,
As you know, "Number of cases ’correctly predicted’" in a logit/probit model can be miss-leading even in a 50/50 split case.
What we should be comparing is not zero ‘correctly predicted’, but rather random assignments based on sample mean.
If a sample is 50/50 split, a random assignment would get 50% "correctly predicted", in theory. If our model's 'correctly predicted' is 70%, we are only 20 percentage points higher than a model based on random assignment, representing an improvement over the random assignment model by only 40%.
Thus, I would like to propose an alternative output from gretl, i.e. the “Extra number of cases 'correctly predicted' over random assignment” (or something like that), call this dot_R-square perhaps.
Dot_R-saure = (Y_hat_model - Y_hat_random) / (1-Y_hat_random)
where Y_hat_model = sum(Y_hat_model_i=Y_i)/N
Y_hat_random = Y_hat^2 + (1-Y_hat)^2
Y_hat is the sample mean
Y_hat_model_i = Pro(Y_i = 1) >Y_hat
Pro(Y_i = 1) >Y_hat = 1, if Pro(Y_i = 1) >Y_hat is true
Unlike the McFadden R-squared, the interpretation of this is fairly straight forward, i.e. the percent that our model is better off than a model based on random assignment.
Best,
Fred
3 years, 3 months
deseas function arima option
by Javier Sansa
I have been trying to use the different options of the deseas function in
gretl. All the options I used seem to work as intended except the arima
option. To be more specific I have tried to build a deseasonalizing
procedure in which a certain arima model is imposed. According to the gretl
reference manual this should be done by building a bundle and using "arima"
and a 6x1 matrix which defines the model, like in the following example:
matrix start ={0,2,1,0,1,1}
bundle b=defbundle(verbose=2,seats=1,outliers=7, arima=start)
The bundle definition works, i.e a bundle b is built, but function deseas
fails because it does not recognize the arima key
I attach a file with data and a short script should something be unclear.
Please, let me know if I failed to follow the manual or there is something
strange going on with the program.
And since we are at the end of 2022, a happy new year!
Javier Sansa
3 years, 3 months
count panel data - poisson model with dummies?
by lars.ahnland＠outlook.com
Hi,
I have count data with what appears to be poisson distribution of the dependent variable in panel data. There is no automatic method for doing this in Gretl as I have understood it, so I am using a count, poisson, model with cross-sectional dummies. Gretl deletes the last dummy so it becomes a fixed effects model, so intuitively, it looks as if the model should be ok. Do you agree? One problem is that Gretl fails to include an overdispersion (Chi-square) test, so I am not sure the poisson model is valid. Can you help me with this too?
Best regards,
Lars Ahnöand
3 years, 3 months
Function package DHF_test is going to be retired
by Sven Schreiber
Hello everybody,
this is to let you know that the contributed function package "DHF_test"
is going to be removed from the public package server soon. If you rely
on that package for whatever reason, you can keep a local copy on your
system(s). (And also perhaps write us about the fact that you're using
it, which would be interesting as well.)
The package DHF_test implements an early test for seasonal unit roots,
its name stands for Dickey-Hasza-Fuller. But for seasonal unit root
testing we currently recommend to use the "GHegy" package instead, which
also has been available for many years on the gretl package server, and
which refers to the HEGY publication (Hylleberg/Engle/Granger/Yoo, 1990).
DHF_test package author Marcin obviously knows about this
reorganization, as he is also a member of the gretl team.
If you have any remaining questions about this (and perhaps also about
seasonal unit root testing in gretl), please go ahead and ask them.
cheers
sven
3 years, 3 months
