SVAR Restriction
by Olasehinde Timmy
Dear Prof,
I am aware that the code 'SVAR_restrict(&x, "C", 1, 2, 0)' is to inform
Gretl of the nature of the restriction to estimate. I also know that this
has to be repeated for several restrictions like in the model. However, I
consider this to be rigorous especially when a large matrix is involved.
Although, I know how a matrix can be created and used with the SVAR GUI, I
don't know how it can be used with the SVAR_restrict(&x, ) function.
Please, I need to be enlighten if this is possible or if I have to do the
imputation for each restriction.
Thanks.
2 months, 3 weeks
Midas make high frequency series stationnary
by d.lalountas＠minfin.gr
HI all,
I am working with non stationary HF data . The source of stationnarity appers to by a seasonal patern.
The ordinary gretl functions to deal with non stationary patterns are diff sdiff , while the corresponding diff function is hfdiff/hfldiff.
In the midas case what are the corresponding function to deal with seasonality?
Thanks in advance
Denis
11 months, 3 weeks
chow test
by Alison Loddick
Hi all,
I’m hoping the econometricians can help me. I’m working with a colleague at another university and he recommends using the Chow test to test whether we should use an OLS model vs Fixed effects. My question is what test does Gretl use within Fixed effects to compare the model with OLS as the manual doesn’t say?
The manual recommends that we use the fixed effect model results to see whether there should be a fixed effects model or OLS. I assumed this was the Chow test. My output is below:
Test for differing group intercepts -
Null hypothesis: The groups have a common intercept
Test statistic: F(98, 1230) = 11.49
with p-value = P(F(98, 1230) > 11.49) = 4.27683e-116
There is a Chow test on the OLS model but when I run it has different degrees of freedom and different F-statistic.
Thank you for any knowledge you can give me.
You all have been great in increasing my knowledge.
Alison
University of Northampton: Transforming Lives and Inspiring Change www.northampton.ac.uk This e-mail is private and may be confidential and is for the intended recipient only. If you are not the intended recipient you are strictly prohibited from using, printing, copying, distributing or disseminating this e-mail or any information contained in it. We virus scan all E-mails leaving The University of Northampton but no warranty is given that this E-mail and any attachments are virus free. You should undertake your own virus checking. The right to monitor E-mail communications through our networks is reserved by us.
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11 months, 3 weeks
First time user of Hansl and creating a matrix of Spearmans correlation
by Alison Loddick
Hi All,
I have never used Hansl or Gauss before and I am trying to work out how to get a programme working to create a matrix of spearman correlations. Allin created one last month, which I tried to run and kept getting 'No redo information available'. Rather than coming running and asking for help I went to Chat GPT asked it what it meant which has gone over my head. I also asked it to decrible each line of the programme as I've done some programming decades ago. So I asked Chat gpt to create me a programme which is very similar to Allin's but with my filename in and variable names in. I get the same error. I'm wondering if someone could direct me into what I should be doing. My data file is Panel2.gdt and I have four variable ROE, ENV,SOC and GOV. It is a panel datafile which is already set up as panel data. The programme from ChatGPT is the following. Please can someone be helpful to a novice I'm sure there is something very simple that I don't know what to do.
// Function to calculate Spearman correlations and p-values for a list of variables
function matrix spearman_matrix(list variables)
matrix SC = {};
strings rnames = array(0);
n = nelem(variables);
loop i = 1..n
loop j = 1..i-1
// Calculate Spearman correlation and p-value
SC |= npcorr(variables[j], variables[i], spearman);
// Construct row names
rnames += sprintf("%s_%s", varname(variables[j]), varname(variables[i]));
endloop
endloop
// Set column names
cnameset(SC, defarray("Spearman", "t-stat", "p-value"));
// Set row names
rnameset(SC, rnames);
return SC;
end function;
// Open your dataset
open panel2.gdt;
// Specify the variables of interest
list variables = deflist("ROE", "ENV", "SOC", "GOV");
// Calculate Spearman correlations and p-values
matrix SC_matrix = spearman_matrix(variables);
// Print the resulting matrix
print SC_matrix;
Thank you for any help
Alison
University of Northampton: Transforming Lives and Inspiring Change www.northampton.ac.uk This e-mail is private and may be confidential and is for the intended recipient only. If you are not the intended recipient you are strictly prohibited from using, printing, copying, distributing or disseminating this e-mail or any information contained in it. We virus scan all E-mails leaving The University of Northampton but no warranty is given that this E-mail and any attachments are virus free. You should undertake your own virus checking. The right to monitor E-mail communications through our networks is reserved by us.
Disclaimer
The information contained in this communication from the sender is confidential. It is intended solely for use by the recipient and others authorized to receive it. If you are not the recipient, you are hereby notified that any disclosure, copying, distribution or taking action in relation of the contents of this information is strictly prohibited and may be unlawful.
This email has been scanned for viruses and malware, and may have been automatically archived by Mimecast, a leader in email security and cyber resilience. Mimecast integrates email defenses with brand protection, security awareness training, web security, compliance and other essential capabilities. Mimecast helps protect large and small organizations from malicious activity, human error and technology failure; and to lead the movement toward building a more resilient world. To find out more, visit our website.
11 months, 3 weeks
Data File Collection Added : "An Introduction to Statistical Learning"
by Artur T.
Dear Gretl Users,
We are excited to announce that the Gretl team has added a new data file
collection to our resources. This collection includes datasets from the
well-known book "An Introduction to Statistical Learning" by Gareth
James, Daniela Witten, Trevor Hastie, Robert Tibshirani, and Jonathan
Taylor, published by Springer.
We express our profound gratitude to the authors for granting permission
to use this valuable collection. You can learn more about the book here:
https://link.springer.com/book/10.1007/978-3-031-38747-0
The new datasets are accessible via the Gretl interface. Here are the
steps to download the collection:
1. Click on "File" -> "Open data"
2. Select "Sample file"
3. Press the "Look on the server" button at the left top
4. Left-click on the desired file and click "install"
5. Re-start Gretl to have the dataset available for use.
For an overview of all datasets available for Gretl, please visit:
https://gretl.sourceforge.net/gretl_data.html
We hope that this new collection will prove useful in your research and
learning activities. As always, we appreciate your continued support for
Gretl and look forward to bringing more resources to enhance your
experience.
Best regards,
Artur
11 months, 4 weeks
something like a range slider possible with gnuplot?
by Sven Schreiber
Hi everybody,
what I'm asking is maybe best explained by example, see the script
below, which calls R. Is something like that possible with gnuplot (and
hence more directly linkable to gretl) ?
thanks
sven
<hansl>
# The following produces a dynamic browser-based plot
# with a slider at the bottom to choose the range
open denmark
foreign language=R --send-data
library(dygraphs) # in turn depends on "magrittr"
moneyincome <- cbind(gretldata[,"LRM"], gretldata[,"LRY"])
dygraph(moneyincome) %>% dyRangeSelector()
end foreign
</hansl>
12 months
Updates to the Gretl Project Wiki on GitHub
by Artur T.
Dear Gretl Users,
I am thrilled to announce that we have made significant updates to the
Gretl Project Wiki on GitHub. The wiki has been enriched with a wealth
of new materials and examples, making it an even more valuable resource
for Gretl users of all experience levels.
You can check out the updated Wiki here:
https://github.com/gretl-project/material-on-gretl/wiki
Here's a quick overview of the key updates:
Plotting:
We've added numerous examples that illustrate various ways to create and
customize plots using Gretl. These examples aim to guide you in
effectively visualizing your data and results.
Data Handling
The updated wiki now includes comprehensive examples on data
manipulation and management in Gretl. These examples cover a wide range
of scenarios and will help you handle your data more efficiently.
Descriptive Statistics
Our expanded section on descriptive statistics provides a plethora of
examples demonstrating how to calculate statistical measures.
Input-Output Information
Finally, we've added a plethora of examples related to handling input
and output in Gretl. These examples will assist you in reading, writing,
and managing your data and results effectively.
Thank you for being part of our Gretl user community. We look forward to
your feedback and suggestions on the updated wiki. If you have any
questions or need assistance with any aspect of Gretl, don't hesitate to
reach out.
Lastly, don't forget that there exists another Gretl-Wiki page which
gets updated from time to time:
https://gretlwiki.econ.univpm.it/index.php/Main_Page
Best,
Artur
1 year
BVAR 0.2 available
by Luca Pedini
Hi everybody,
a new version of BVAR has just been released!
The major changes are:
* gridplot integration via the bundle key “grid” in the plotting functions;
* customizable “confidence bands” for both forecasts and irfs via the bundle key “q_range”
* the printout function will now report frequentist estimates (when available) alongside the Bayesian ones.
Stay tuned for other updates!
Best regards,
Luca Pedini
1 year
Saving to .CSV from FOREIGN{} routine
by Meridiana GeoTopo
Hi there.
I encountered an issue that puzzled me while trying to save an R dataset
data to re-open in Gretl from inside foreign {} end foreign
gretl.export(x) saves in .csv but loses information (column and rows, keeps
only the central data). I tried many set.
As an example, the native AirPassengers dataset:
/* ------------ script ----------------*/
set R_functions on
foreign language=R
library(ggplot2)
library(ggfortify)
library(remotes)
autoplot(AirPassengers)
p<-autoplot(AirPassengers, ts.colour = 'blue')
p + ggtitle('AirPassengers') + xlab('Year') + ylab('Passengers')
data <- as.matrix(AirPassengers) #fails
gretl.export(data)
#data <- data.frame(AirPassengers)
#save(data, file = "C:/User/USUARIO/AppData/Roaming/gret/AirPassengers.csv")
#gretl.export(AirPassengers) #fails
#write.csv(AirPassengers, file =
"C:/Users/USUARIO/AppData/Roaming/gret/AirPassengers.csv", sep=";" , dec=
",", append=FALSE, row.names=TRUE) #fails
#write.csv(AirPassengers, file =
"C:/User/USUARIO/AppData/Roaming/gret/AirPassengers.csv", row.names=FALSE,
quote=FALSE) #fails
#write_xlsx also fails
end foreign
open C:/Users/USUARIO/AppData/Roaming/gretl/AirPassengers.csv #opens set
in gretl
/*-----------------------------------*/
Only gretl.export SAVES to disk. But saves the wrong data.
I am attaching what the set should look like.
Anyone knows how to work around this?
Many thanks
Tony
1 year
Merging data frames with different number of rows without repeated ID
by Dhanasekaran Kuppusamy
Dear Gretl community
*, * I have two datasets A and B.
Both A and B have different variables but with common variable i.e. ID. I
want to merge these two data sets using ID as the common variable as given
below : data set C. Thanking you in advanceI would be thankful for
providing the solution. Note: A : Household data and B members data.
Dhanasekaran K
Merging data frames with different number of rows
<https://community.rstudio.com/t/merging-data-frames-with-different-number...>
<https://community.rstudio.com/c/tidyverse/6>data set:A
*ID*
*x1*
*x2*
*x3*
1
50
2
1
2
60
5
3
3
30
6
5
4
20
9
6
5
90
7
9
data set:B
*ID*
*x4*
*x5*
1
5
male
1
4
female
1
3
male
2
2
male
2
5
female
3
6
male
3
1
female
3
8
female
4
2
male
5
3
female
5
10
male
merged data set: C
ID
x1
x2
x3
x4.1
x4.2
x4.3
x5.1
x5.2
x5.3
1
50
2
1
5
4
3
male
female
male
2
60
5
3
2
5
NA
male
female
NA
3
30
6
5
6
1
8
male
female
female
4
20
9
6
2
NA
NA
male
NA
NA
5
90
7
9
3
10
NA
female
male
NA
1 year
