5:45 p.m.
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
9:23 p.m.
New subject: How to apply the HAC option in Gretl?
Hi Allin Cottrell,
Thank a lot for your suggestion. It works! My data are not "perfect" due to
missing values, and some zeros seem to cause some trouble when taking the natural log of
the dependent variable. The data are defined as time series data when uploaded into Gretl,
but missing values cause trouble when running an ols regression. The HAC seems to be
deactivated when the data are cleaned (when the rows of missing values are deleted from
the data-file). When these data are deleted, the time interval between the observations is
not consistent. However, when redefining the new, cleaned data, and defining them (data)
as time series, the HAC-option function well.
Maybe the developers of Gretl can look into this problem - or lack of flexibility. R-does
handle it, and also Eviews.
Kind regards,
Tobben
9:47 a.m.
New subject: How to apply the HAC option in Gretl?
Am 07.11.2022 um 22:23 schrieb tobbenlorentzen(a)gmail.com:
Hi Allin Cottrell,
Thank a lot for your suggestion. It works! My data are not "perfect" due to
missing values, and some zeros seem to cause some trouble when taking the natural log of
the dependent variable. The data are defined as time series data when uploaded into Gretl,
but missing values cause trouble when running an ols regression. The HAC seems to be
deactivated when the data are cleaned (when the rows of missing values are deleted from
the data-file). When these data are deleted, the time interval between the observations is
not consistent. However, when redefining the new, cleaned data, and defining them (data)
as time series, the HAC-option function well.
Maybe the developers of Gretl can look into this problem - or lack of flexibility. R-does
handle it, and also Eviews.
Hi, don't know about R, but what you're saying about Eviews is only true
up to a certain extent. I just checked on Eviews 11 (not current, I
know, anybody please speak up if the situation has changed since then);
yes, Eviews automatically removes the interior missing values for the
OLS estimate, which saves you the hassle of removing them manually.
(Note that if you have a bunch of lags, this can lead to a substantial
"hole" in your estimation sample, but I guess the user is supposed to be
aware of that.)
However, if you then let Eviews run diagnostic tests on the estimated
residual "gappy" time series, it does carry them out, but tells you:
"interior missing value lagged residuals set to zero". This is of course
a pragmatic approach (and asymptotically justified), but will bias the
results in finite samples. This can also affect the estimates of the HAC
standard errors.
Maybe this way of handling it is preferable, given that other
alternatives will also be imperfect. But there is some arbitrariness
involved in the approach of Eviews.
cheers
sven
10:41 a.m.
New subject: How to apply the HAC option in Gretl?
Hi Sven,
Thank for your comments. I agree with you that “gappy” time series clearly corrupt a valid
evaluation of the model, and the estimates – depending on the amount of data gabs
(systematically or non-systematically). But what shall we do when we only have access to
incomplete data? My ad-hoc point is that if we detect heterosc/autocorr by using the
incomplete time series, it is necessary to correct the st.dev. of the coefficient
estimate. I experienced that it was difficult to activate the HAC in Gretl when the data
are incomplete, and to what I could see, the HAC-option was deactivated when I used “Drop
observations with missing values”. I’m a new user of Gretl. I’m impressed over the job the
developers have done, and I look forward using the program 😊
Regards,
Torbj.
Fra: Sven Schreiber <sven.schreiber(a)fu-berlin.de>
Sendt: 8. november 2022 10:48
Til: gretl-users(a)gretlml.univpm.it
Emne: [Gretl-users] Re: How to apply the HAC option in Gretl?
Am 07.11.2022 um 22:23 schrieb tobbenlorentzen(a)gmail.com
<mailto:tobbenlorentzen@gmail.com> :
Hi Allin Cottrell,
Thank a lot for your suggestion. It works! My data are not "perfect" due to
missing values, and some zeros seem to cause some trouble when taking the natural log of
the dependent variable. The data are defined as time series data when uploaded into Gretl,
but missing values cause trouble when running an ols regression. The HAC seems to be
deactivated when the data are cleaned (when the rows of missing values are deleted from
the data-file). When these data are deleted, the time interval between the observations is
not consistent. However, when redefining the new, cleaned data, and defining them (data)
as time series, the HAC-option function well.
Maybe the developers of Gretl can look into this problem - or lack of flexibility. R-does
handle it, and also Eviews.
Hi, don't know about R, but what you're saying about Eviews is only true up to a
certain extent. I just checked on Eviews 11 (not current, I know, anybody please speak up
if the situation has changed since then); yes, Eviews automatically removes the interior
missing values for the OLS estimate, which saves you the hassle of removing them manually.
(Note that if you have a bunch of lags, this can lead to a substantial "hole" in
your estimation sample, but I guess the user is supposed to be aware of that.)
However, if you then let Eviews run diagnostic tests on the estimated residual
"gappy" time series, it does carry them out, but tells you: "interior
missing value lagged residuals set to zero". This is of course a pragmatic approach
(and asymptotically justified), but will bias the results in finite samples. This can also
affect the estimates of the HAC standard errors.
Maybe this way of handling it is preferable, given that other alternatives will also be
imperfect. But there is some arbitrariness involved in the approach of Eviews.
cheers
sven
12:30 p.m.
New subject: How to apply the HAC option in Gretl?
On Tue, 8 Nov 2022, Sven Schreiber wrote:
However, if you then let Eviews run diagnostic tests on the estimated
residual "gappy" time series, it does carry them out, but tells you:
"interior missing value lagged residuals set to zero". This is of course a
pragmatic approach (and asymptotically justified), but will bias the results
in finite samples. This can also affect the estimates of the HAC standard
errors.
Maybe this way of handling it is preferable, given that other alternatives
will also be imperfect. But there is some arbitrariness involved in the
approach of Eviews.
Granted, there is, but I guess it's better than nothing, provided the user
is notified appropriately.
In my local tree I patched the source so as to do what I think eviews
does; if anyone's curious, the patch is attached.
By running the following piece of code
<hansl>
nulldata 64
set seed 20221109
setobs 1 1 --special-time-series
x = uniform() < 0.9 ? normal() : NA
y = uniform() < 0.9 ? normal() : NA
ols y const x --robust
</hansl>
I'm getting the following output:
<output>
Model 1: OLS, using observations 1-64 (T = 53)
Missing or incomplete observations dropped: 11
Dependent variable: y
HAC standard errors, bandwidth 2 (Bartlett kernel)
coefficient std. error t-ratio p-value
--------------------------------------------------------
const -0.536282 0.122625 -4.373 6.05e-05 ***
x -0.0521759 0.175615 -0.2971 0.7676
Mean dependent var -0.529945 S.D. dependent var 1.023859
Sum squared resid 54.38531 S.E. of regression 1.032656
R-squared 0.002306 Adjusted R-squared -0.017257
F(1, 51) 0.088271 P-value(F) 0.767594
Log-likelihood -75.88750 Akaike criterion 155.7750
Schwarz criterion 159.7156 Hannan-Quinn 157.2904
</output>
Sven (or any other): could you cross-check against eviews to see if
results match?
-------------------------------------------------------
Riccardo (Jack) Lucchetti
Dipartimento di Scienze Economiche e Sociali (DiSES)
Università Politecnica delle Marche
(formerly known as Università di Ancona)
r.lucchetti(a)univpm.it
http://www2.econ.univpm.it/servizi/hpp/lucchetti
-------------------------------------------------------
5:03 p.m.
New subject: How to apply the HAC option in Gretl?
Am 09.11.2022 um 13:30 schrieb Riccardo (Jack) Lucchetti:
On Tue, 8 Nov 2022, Sven Schreiber wrote:
>
> Maybe this way of handling it is preferable, given that other
> alternatives will also be imperfect. But there is some arbitrariness
> involved in the approach of Eviews.
Granted, there is, but I guess it's better than nothing, provided the
user is notified appropriately.
I'm getting the following output:
<output>
Model 1: OLS, using observations 1-64 (T = 53)
Missing or incomplete observations dropped: 11
Dependent variable: y
HAC standard errors, bandwidth 2 (Bartlett kernel)
coefficient std. error t-ratio p-value
--------------------------------------------------------
const -0.536282 0.122625 -4.373 6.05e-05 ***
x -0.0521759 0.175615 -0.2971 0.7676
...
Sven (or any other): could you cross-check against eviews to see if
results match?
No, not quite. Even if I force the bandwidth to 2 in Eviews (it would
automatically choose 4 here), and switch off the d.f. adjustment, the
standard errors do not match. (Coefficients do match.) I get 0.133099
and 0.140418 instead.
This is still with Eviews 11.
cheers
sven
5:32 p.m.
New subject: How to apply the HAC option in Gretl?
On Thu, Nov 10, 2022 at 12:03 PM Sven Schreiber
<sven.schreiber(a)fu-berlin.de> wrote:
Am 09.11.2022 um 13:30 schrieb Riccardo (Jack) Lucchetti:
> On Tue, 8 Nov 2022, Sven Schreiber wrote:
>
>>
>> Maybe this way of handling it is preferable, given that other
>> alternatives will also be imperfect. But there is some arbitrariness
>> involved in the approach of Eviews.
>
> Granted, there is, but I guess it's better than nothing, provided the
> user is notified appropriately.
>
>
> I'm getting the following output:
>
> <output>
> Model 1: OLS, using observations 1-64 (T = 53)
> Missing or incomplete observations dropped: 11
> Dependent variable: y
> HAC standard errors, bandwidth 2 (Bartlett kernel)
>
> coefficient std. error t-ratio p-value
> --------------------------------------------------------
> const -0.536282 0.122625 -4.373 6.05e-05 ***
> x -0.0521759 0.175615 -0.2971 0.7676
> ...
>
> Sven (or any other): could you cross-check against eviews to see if
> results match?
>
No, not quite. Even if I force the bandwidth to 2 in Eviews (it would
automatically choose 4 here), and switch off the d.f. adjustment, the
standard errors do not match. (Coefficients do match.) I get 0.133099
and 0.140418 instead.
I guess the next question is: do the Newey-West results agree when
there are no missing values?
Allin Cottrell
7:57 p.m.
New subject: How to apply the HAC option in Gretl?
On Thu, Nov 10, 2022 at 12:03 PM Sven Schreiber
<sven.schreiber(a)fu-berlin.de> wrote:
Am 09.11.2022 um 13:30 schrieb Riccardo (Jack) Lucchetti:
>
> I'm getting the following output:
>
> <output>
> Model 1: OLS, using observations 1-64 (T = 53)
> Missing or incomplete observations dropped: 11
> Dependent variable: y
> HAC standard errors, bandwidth 2 (Bartlett kernel)
>
> coefficient std. error t-ratio p-value
> --------------------------------------------------------
> const -0.536282 0.122625 -4.373 6.05e-05 ***
> x -0.0521759 0.175615 -0.2971 0.7676
> ...
>
> Sven (or any other): could you cross-check against eviews to see if
> results match?
>
No, not quite. Even if I force the bandwidth to 2 in Eviews (it would
automatically choose 4 here), and switch off the d.f. adjustment, the
standard errors do not match. (Coefficients do match.) I get 0.133099
and 0.140418 instead.
I can't do the Eviews comparison, but I've looked into R: lm() for
estimation and NeweyWest() from the sandwich package for HAC standard
errors. Using the new HAC_ALLOW_MISSING setting, here's what I found:
* With no missing values, gretl and R produce HAC results identical to
machine precision (given the same HAC parameters).
* With missing values, the HAC results differ noticeably, and it turns
out that R is ignoring the missing values altogether. I can replicate
the R results in gretl if after sprinkling the NAs I do "smpl
--no-missing" then reestablish the time-series property with "setobs 1
1 --special-time-series". I wonder if that could be the case with
Eviews too.
Allin
8:58 p.m.
New subject: How to apply the HAC option in Gretl?
On Thu, 10 Nov 2022, Cottrell, Allin wrote:
* With missing values, the HAC results differ noticeably, and it
turns
out that R is ignoring the missing values altogether. I can replicate
the R results in gretl if after sprinkling the NAs I do "smpl
--no-missing" then reestablish the time-series property with "setobs 1
1 --special-time-series".
IMHO from a statistical point of view this makes no sense whatsoever.
I wonder if that could be the case with Eviews too.
Probably not. If that was the case, modifying my little example script as
in
<hansl>
nulldata 64
set seed 20221109
x = uniform() < 0.9 ? normal() : NA
y = uniform() < 0.9 ? normal() : NA
smpl --no-missing
setobs 1 1 --special-time-series
ols y const x --robust
</hansl>
would produce the same standard errors that Sven reported earlier today
(and doesn't).
-------------------------------------------------------
Riccardo (Jack) Lucchetti
Dipartimento di Scienze Economiche e Sociali (DiSES)
Università Politecnica delle Marche
(formerly known as Università di Ancona)
r.lucchetti(a)univpm.it
http://www2.econ.univpm.it/servizi/hpp/lucchetti
-------------------------------------------------------
9:04 p.m.
New subject: How to apply the HAC option in Gretl?
Am 10.11.2022 um 18:32 schrieb Cottrell, Allin:
On Thu, Nov 10, 2022 at 12:03 PM Sven Schreiber
<sven.schreiber(a)fu-berlin.de> wrote:
>
> No, not quite. Even if I force the bandwidth to 2 in Eviews (it would
> automatically choose 4 here), and switch off the d.f. adjustment, the
> standard errors do not match. (Coefficients do match.) I get 0.133099
> and 0.140418 instead.
I guess the next question is: do the Newey-West results agree when
there are no missing values?
Yeah, good point of course. No, they don't. For some newly drawn sample
data it's 0.125 vs. 0.114 (Eviews/gretl) and 0.1034 vs. 0.0889.
Results are much closer when I let Eviews do "Newey-West fixed bandwidth
= 4.0000". Could it be that gretl's parameter terminology uses the
square root of what Eviews does? (Bartlett kernel in both cases.)
thanks
sven
9:07 p.m.
New subject: How to apply the HAC option in Gretl?
On Thu, 10 Nov 2022, Sven Schreiber wrote:
Am 10.11.2022 um 18:32 schrieb Cottrell, Allin:
> On Thu, Nov 10, 2022 at 12:03 PM Sven Schreiber
> <sven.schreiber(a)fu-berlin.de> wrote:
>>
>> No, not quite. Even if I force the bandwidth to 2 in Eviews (it would
>> automatically choose 4 here), and switch off the d.f. adjustment, the
>> standard errors do not match. (Coefficients do match.) I get 0.133099
>> and 0.140418 instead.
> I guess the next question is: do the Newey-West results agree when
> there are no missing values?
>
Yeah, good point of course. No, they don't. For some newly drawn sample data
it's 0.125 vs. 0.114 (Eviews/gretl) and 0.1034 vs. 0.0889.
Ah, ok, sorry. My earlier comment on eviews obviously doesn't apply then.
Results are much closer when I let Eviews do "Newey-West fixed
bandwidth =
4.0000". Could it be that gretl's parameter terminology uses the square root
of what Eviews does? (Bartlett kernel in both cases.)
More likely, the two-sided bandwidth (but this is just a wild guess).
-------------------------------------------------------
Riccardo (Jack) Lucchetti
Dipartimento di Scienze Economiche e Sociali (DiSES)
Università Politecnica delle Marche
(formerly known as Università di Ancona)
r.lucchetti(a)univpm.it
http://www2.econ.univpm.it/servizi/hpp/lucchetti
-------------------------------------------------------
9:37 p.m.
New subject: How to apply the HAC option in Gretl?
Am 10.11.2022 um 22:07 schrieb Riccardo (Jack) Lucchetti:
On Thu, 10 Nov 2022, Sven Schreiber wrote:
> Results are much closer when I let Eviews do "Newey-West fixed
> bandwidth = 4.0000". Could it be that gretl's parameter terminology
> uses the square root of what Eviews does? (Bartlett kernel in both
> cases.)
More likely, the two-sided bandwidth (but this is just a wild guess).
Ah, I got it now. The Eviews documentation says "the bandwidth is equal
to one plus what Stock and Watson term the 'truncation parameter' m". So
I set it to 3 in Eviews (given the 2 in gretl), and still no d.f.
correction. Then the results are the same for Eviews and gretl, without
missing data.
With the gappy data I still have mismatch even under the new bandwidth
parameter. I now have with Eviews 0.1274 and 0.1444. (Where Jack's
results were 0.1226 and 0.1756.)
But I haven't tried to understand what you, Jack, did - did you also try
to replace the missing residuals with zeros?
cheers
sven
9:50 p.m.
New subject: How to apply the HAC option in Gretl?
On Thu, 10 Nov 2022, Sven Schreiber wrote:
But I haven't tried to understand what you, Jack, did - did you
also try to
replace the missing residuals with zeros?
Yes.
-------------------------------------------------------
Riccardo (Jack) Lucchetti
Dipartimento di Scienze Economiche e Sociali (DiSES)
Università Politecnica delle Marche
(formerly known as Università di Ancona)
r.lucchetti(a)univpm.it
http://www2.econ.univpm.it/servizi/hpp/lucchetti
-------------------------------------------------------
10:20 p.m.
New subject: How to apply the HAC option in Gretl?
Hi all,
I think it is not consistent to substitute missing values with zeros, because zeros are
time-dated value. Missing values are genuine non-information, and it could be anything
(except if the data have some structure).
Regards,
Torbjørn
Fra: Sven Schreiber <sven.schreiber(a)fu-berlin.de>
Sendt: 10. november 2022 22:38
Til: gretl-users(a)gretlml.univpm.it
Emne: [Gretl-users] Re: How to apply the HAC option in Gretl?
Am 10.11.2022 um 22:07 schrieb Riccardo (Jack) Lucchetti:
On Thu, 10 Nov 2022, Sven Schreiber wrote:
Results are much closer when I let Eviews do "Newey-West fixed bandwidth =
4.0000". Could it be that gretl's parameter terminology uses the square root of
what Eviews does? (Bartlett kernel in both cases.)
More likely, the two-sided bandwidth (but this is just a wild guess).
Ah, I got it now. The Eviews documentation says "the bandwidth is equal to one plus
what Stock and Watson term the 'truncation parameter' m". So I set it to 3 in
Eviews (given the 2 in gretl), and still no d.f. correction. Then the results are the same
for Eviews and gretl, without missing data.
With the gappy data I still have mismatch even under the new bandwidth parameter. I now
have with Eviews 0.1274 and 0.1444. (Where Jack's results were 0.1226 and 0.1756.)
But I haven't tried to understand what you, Jack, did - did you also try to replace
the missing residuals with zeros?
cheers
sven
7:11 a.m.
New subject: How to apply the HAC option in Gretl?
Am 10.11.2022 um 23:20 schrieb tobbenlorentzen(a)gmail.com:
Hi all,
I think it is not consistent to substitute missing values with zeros,
because zeros are time-dated value. Missing values are genuine
non-information, and it could be anything (except if the data have
some structure).
Well, that's exactly why I said that this approach of Eviews (and other
software) is somewhat arbitrary and it's not simply a case of an
obviously missing feature on our side.
However, notice that we're talking of missing _residuals_ after
estimation here, not of replacing the missing values of the series
themselves with zeros.
cheers
sven
1:25 p.m.
New subject: robust time-series inference with gappy data (was Re: Re: How to apply the HAC option in Gretl?)
Am 10.11.2022 um 20:57 schrieb Cottrell, Allin:
I can't do the Eviews comparison, but I've looked into R:
lm() for
estimation and NeweyWest() from the sandwich package for HAC standard
errors. Using the new HAC_ALLOW_MISSING setting, here's what I found:
Just as a general update to all users out there, Allin has worked hard
(as usual) to implement this functionality in gretl, and in the current
development source code it is there and working. (The original problem
was that when the time series contain gaps and thus observations are not
fully contiguous, computing Newey-West robust standard errors/covariance
estimates is not directly possible, so some more or less arbitrary but
justifiable workarounds have to be used.)
As always with gretl, this will be duly documented. It is not yet in the
current snapshots, I believe. We will probably ask you guys to test the
new feature when updated snapshots for Mac and Windows are available.
thanks
sven
7:24 p.m.
New subject: robust time-series inference with gappy data (was Re: Re: How to apply the HAC option in Gretl?)
Thank a lot to Allin and Sven for the programming work and making Gretl more flexible 😊 I
presume the new feature is implemented in the Gretl 2022c-version.
Torbj.
-----Opprinnelig melding-----
Fra: Sven Schreiber <sven.schreiber(a)fu-berlin.de>
Sendt: 4. januar 2023 14:25
Til: gretl-users(a)gretlml.univpm.it
Emne: [Gretl-users] robust time-series inference with gappy data (was Re: Re: How to apply
the HAC option in Gretl?)
Am 10.11.2022 um 20:57 schrieb Cottrell, Allin:
I can't do the Eviews comparison, but I've looked into R:
lm() for
estimation and NeweyWest() from the sandwich package for HAC standard
errors. Using the new HAC_ALLOW_MISSING setting, here's what I found:
Just as a general update to all users out there, Allin has worked hard (as usual) to
implement this functionality in gretl, and in the current development source code it is
there and working. (The original problem was that when the time series contain gaps and
thus observations are not fully contiguous, computing Newey-West robust standard
errors/covariance estimates is not directly possible, so some more or less arbitrary but
justifiable workarounds have to be used.)
As always with gretl, this will be duly documented. It is not yet in the current
snapshots, I believe. We will probably ask you guys to test the new feature when updated
snapshots for Mac and Windows are available.
thanks
sven
_______________________________________________
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8:20 p.m.
New subject: robust time-series inference with gappy data (was Re: Re: How to apply the HAC option in Gretl?)
Am 11.01.2023 um 20:24 schrieb tobbenlorentzen(a)gmail.com:
Thank a lot to Allin and Sven for the programming work and making
Gretl more flexible 😊 I presume the new feature is implemented in the Gretl 2022c-version.
I can't take credit for the programming work here; in any case, gretl
2022c is the current version and does not have it yet. But 2023a will.
In the meantime, the recent snapshots also have it, so you can try out
the feature there. For example, let's take again Jack's little script:
<hansl>
nulldata 64
set seed 20221109
setobs 1 1 --special-time-series
x = uniform() < 0.9 ? normal() : NA
y = uniform() < 0.9 ? normal() : NA
ols y const x --robust
</hansl>
Here's the output I'm getting with a snapshot (on Windows) from Jan 7th
(sorry for the German, but you get the point):
<output>
Modell 1: KQ, benutze die Beobachtungen 1-64 (T = 53)
Fehlende oder unvollständige Beobachtungen entfernt: 11
Abhängige Variable: y
HAC Standardfehler, Bandbreite 2, Bartlett-Kern
Observations not contiguous: ES method used
Koeffizient Std.-fehler t-Quotient p-Wert
------------------------------------------------------------
const −0.536282 0.114281 −4.693 2.06e-05 ***
x −0.0521759 0.151110 −0.3453 0.7313
Mittel abhängige Var. −0.529945 Stdabw. abhängige Var. 1.023859
Summe quad. Residuen 54.38531 Stdfehler Regression 1.032656
R-Quadrat 0.002306 Korrigiertes R-Quadrat -0.017257
F(1, 51) 0.119221 P-Wert(F) 0.731301
Log-Likelihood −75.88750 Akaike-Kriterium 155.7750
Schwarz-Kriterium 159.7156 Hannan-Quinn-Kriterium 157.2904
</output>
But this whole thing also works in the GUI.
cheers
sven
8:40 p.m.
New subject: robust time-series inference with gappy data (was Re: Re: How to apply the HAC option in Gretl?)
It’s a great team behind Gretl. Thank for the script.
Torbj.
Fra: Sven Schreiber <sven.schreiber(a)fu-berlin.de>
Sendt: 11. januar 2023 21:20
Til: gretl-users(a)gretlml.univpm.it
Emne: [Gretl-users] Re: robust time-series inference with gappy data (was Re: Re: How to
apply the HAC option in Gretl?)
Am 11.01.2023 um 20:24 schrieb tobbenlorentzen(a)gmail.com
<mailto:tobbenlorentzen@gmail.com> :
Thank a lot to Allin and Sven for the programming work and making Gretl more flexible 😊 I
presume the new feature is implemented in the Gretl 2022c-version.
I can't take credit for the programming work here; in any case, gretl 2022c is the
current version and does not have it yet. But 2023a will.
In the meantime, the recent snapshots also have it, so you can try out the feature there.
For example, let's take again Jack's little script:
<hansl>
nulldata 64
set seed 20221109
setobs 1 1 --special-time-series
x = uniform() < 0.9 ? normal() : NA
y = uniform() < 0.9 ? normal() : NA
ols y const x --robust
</hansl>
Here's the output I'm getting with a snapshot (on Windows) from Jan 7th (sorry for
the German, but you get the point):
<output>
Modell 1: KQ, benutze die Beobachtungen 1-64 (T = 53)
Fehlende oder unvollständige Beobachtungen entfernt: 11
Abhängige Variable: y
HAC Standardfehler, Bandbreite 2, Bartlett-Kern
Observations not contiguous: ES method used
Koeffizient Std.-fehler t-Quotient p-Wert
------------------------------------------------------------
const −0.536282 0.114281 −4.693 2.06e-05 ***
x −0.0521759 0.151110 −0.3453 0.7313
Mittel abhängige Var. −0.529945 Stdabw. abhängige Var. 1.023859
Summe quad. Residuen 54.38531 Stdfehler Regression 1.032656
R-Quadrat 0.002306 Korrigiertes R-Quadrat -0.017257
F(1, 51) 0.119221 P-Wert(F) 0.731301
Log-Likelihood −75.88750 Akaike-Kriterium 155.7750
Schwarz-Kriterium 159.7156 Hannan-Quinn-Kriterium 157.2904
</output>
But this whole thing also works in the GUI.
cheers
sven
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Cottrell, Allin -
Riccardo (Jack) Lucchetti -
Sven Schreiber -
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