retreiving the IRF and FEVD
by Olasehinde Timmy
Dear Profs,
Could you please direct me to how to retrieve the IRF and FEVD after
SVAR/SVECM estimation?
I really appreciate any help you can provide.
Timmy.
1 hour
Time-varying coefficients estimation: New version
by Ekkehart Schlicht
Hi all,
a new version of the TVC package doing time-varying coefficients
estimation in available on the Gretl server, along with an updated
version of the associated graphical backend TVCplots.
As a new feature, a Monto-Carlo check for testing time-variability of
regression coefficients has been added. This may also serve in some
cases to test the assumption of time-invariance in conventional models.
The feature is experimental. Comments are welcome.
Cheers
Ekkehart
Ekkehart Schlicht
Professor emeritus of Economics
Department of Economics
Ludwig-Maximilians-Universität München
www.semverteilung.vwl.uni-muenchen.de/ekkehart
ekkehart.schlicht(a)econ.lmu.de
Tel. +49 (0)8152 9149270
6 days, 2 hours
Statistical advances in science: climatologists make a discovery
by Brian Revell
Climatologists have established that the (AMOC Atlantic meridional
overurning current)) is slowing down.
"The new research, published in the journal Science Advances
<http://www.science.org/doi/10.1126/sciadv.adx4298>, explored four
different ways of using real-world observations to assess the models. They
found a method called ridge regression, which had been little used in
climate science before now, provided the best results." [Guardian newspaper
l
They have also seem to have discovered that dedication to Bayesian
modelling, de rigeur nowadays for publishing in the natural sciences,
clearly has its limitations.
Well done Gretl ! At the forefront of usefulness.
*Brian *
1 week
It is necessary to use robust standard error in random effect estimation
by Ramki S
When I run the code in the stata, the results are matching with gretl only when I specify the robust standard errors. Why is this case?
xtreg ANS FDIENT FDICAP FDIAST FDITUR FDIGDP FDIROTC FDIROFA FDIROS FDIEMP FDICOM FDIWAG GDPgrowth Size, re
>
Random-effects GLS regression Number of obs = 434
Group variable: Id Number of groups = 62
R-squared: Obs per group:
Within = 0.7754 min = 7
Between = 0.0372 avg = 7.0
Overall = 0.1280 max = 7
Wald chi2(13) = 525.11
corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000
------------------------------------------------------------------------------
ANS | Coefficient Std. err. z P>|z| [95% conf. interval]
-------------+----------------------------------------------------------------
FDIENT | .3538051 .1119847 3.16 0.002 .1343191 .5732911
FDICAP | .0560138 .0341982 1.64 0.101 -.0110135 .123041
FDIAST | -.0488365 .027242 -1.79 0.073 -.1022297 .0045567
FDITUR | -.0249275 .0195643 -1.27 0.203 -.0632728 .0134179
FDIGDP | .0761888 .0221277 3.44 0.001 .0328193 .1195583
FDIROTC | -.0140346 .0176176 -0.80 0.426 -.0485645 .0204953
FDIROFA | .0029775 .0053075 0.56 0.575 -.007425 .01338
FDIROS | -.0061958 .0053613 -1.16 0.248 -.0167037 .004312
FDIEMP | .0093037 .0294696 0.32 0.752 -.0484557 .0670631
FDICOM | -.0078266 .0252738 -0.31 0.757 -.0573623 .041709
FDIWAG | -.2533413 .3531677 -0.72 0.473 -.9455373 .4388548
GDPgrowth | .0565587 .0193329 2.93 0.003 .0186669 .0944505
Size | -12.14111 .6405544 -18.95 0.000 -13.39658 -10.88565
_cons | 69.71459 2.813264 24.78 0.000 64.2007 75.22849
-------------+----------------------------------------------------------------
sigma_u | 1.6051608
sigma_e | 1.0781735
rho | .68909913 (fraction of variance due to u_i)
------------------------------------------------------------------------------
Hausman test results are also different.
Hausman test -
Null hypothesis: GLS estimates are consistent
Asymptotic test statistic: Chi-square(13) = 341.943
with p-value = 3.83701e-65
Stata:
Test of H0: Difference in coefficients not systematic
chi2(13) = (b-B)'[(V_b-V_B)^(-1)](b-B)
= -918.16
Warning: chi2 < 0 ==> model fitted on these data
fails to meet the asymptotic assumptions
of the Hausman test; see suest for a
generalized test.
With sigmamore:
Test of H0: Difference in coefficients not systematic
chi2(13) = (b-B)'[(V_b-V_B)^(-1)](b-B)
= 237.83
Prob > chi2 = 0.0000
data:
https://www.dropbox.com/scl/fi/o9copqsun64if9n7vyu4r/2017035-data.dta?rlk...
1 week, 5 days
Missing value handling and correlated random effects CRE model
by Ramki S
I tried to replicate the following stata webpage example in gretl by creating cwc variables for tenure and age. After running, the number of observations are different and race_1 coefficient is very different although other coefficients are comparable. Any suggestions?
https://www.stata.com/stata-news/news39-4/correlated-random-effects-models/
Model 1: Random-effects (GLS), using 28522 observations
Included 4699 cross-sectional units
Time-series length: minimum 1, maximum 15
Dependent variable: ln_wage
Standard errors clustered by unit
coefficient std. error z p-value
---------------------------------------------------------
const 1.07919 0.0308898 34.94 2.07e-267 ***
tm_tenure 0.0586924 0.00210393 27.90 2.93e-171 ***
tm_age 0.0111681 0.00113419 9.847 7.08e-023 ***
Drace_1 0.131393 0.0117410 11.19 4.52e-029 ***
Drace_3 0.236570 0.0592482 3.993 6.53e-05 ***
Mean dependent var 1.675133 S.D. dependent var 0.478001
Sum squared resid 5607.429 S.E. of regression 0.443427
Log-likelihood −17274.26 Akaike criterion 34558.52
Schwarz criterion 34599.81 Hannan-Quinn 34571.80
rho 0.311897 Durbin-Watson 1.017081
'Between' variance = 0.106527
'Within' variance = 0.102584
mean theta = 0.577464
corr(y,yhat)^2 = 0.13988
Joint test on named regressors -
Asymptotic test statistic: Chi-square(4) = 1467.73
with p-value = 0
Breusch-Pagan test -
Null hypothesis: Variance of the unit-specific error = 0
Asymptotic test statistic: Chi-square(1) = 20404.8
with p-value = 0
Data:
https://www.dropbox.com/scl/fi/zd5od9r00esst52obcxtm/nlsdata.dta?rlkey=nu...
1 week, 6 days
Package updates (March 2026)
by Riccardo (Jack) Lucchetti
Dear all,
this message is to inform the community about the activity in our
function package repository: during the month of March 2026, 2 packages
were updated to a new version:
"CSDpanel", by Sven Schreiber and Jörg Breitung (test and estimators for
panel datasets with cross-section dependence)
"TVCplots", by Ekkehart Schlicht (plot time-varying series with
confidence bands)
As usual, update and enjoy!
-------------------------------------------------------
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
-------------------------------------------------------
2 weeks
Grand mean centering, groupmean, cwc variable creation
by Ramki S
Hi
Is there any option to create grand mean centering of a varibale x_mc, (x_i - x_bar), group mean (x_barj), centering within clustered variable x_cwc (x_i - x_barj). These are necesary functions to teach mixed models. If there are not, i request you to provide.
2 weeks, 2 days
OLS Results mis-match
by Ramki S
Hi,
I tried to run simple OLS but the results are not matching. The SPSS results are matching with text book results.
SPSS results:
Coefficients
Model B Std. Error Beta t Sig.
1 (Constant) 1158559.201 360592.720 3.213 .002
Q 2026114.340 61806.945 .907 32.781 .000
PF 1.225 .104 .339 11.814 .000
LF -3065753.131 696327.318 -.136 -4.403 .000
a Dependent Variable: C
GRETL:
Model 2: OLS, using observations 1-90
Dependent variable: C
Omitted due to exact collinearity: LF
coefficient std. error t-ratio p-value
-----------------------------------------------------------------
const 1.58686e+06 154259 10.29 1.05e-016 ***
Q −32683.7 2587.90 −12.63 2.23e-021 ***
PF 2.16835 0.205182 10.57 2.83e-017 ***
Mean dependent var 1122524 S.D. dependent var 1192075
Sum squared resid 3.44e+13 S.E. of regression 628752.4
R-squared 0.728055 Adjusted R-squared 0.721803
F(2, 87) 116.4586 P-value(F) 2.51e-25
Log-likelihood −1327.813 Akaike criterion 2661.626
Schwarz criterion 2669.126 Hannan-Quinn 2664.651
https://www.dropbox.com/scl/fi/ndhl6sauxeeuufzl2wa6x/gujarati_Table16_1_a...
2 weeks, 2 days
Add cross sectional mean is giving time series mean
by Ramki S
I loaded this data into gretl and told that this is time series stacked data. I wanted to create Y_barj varibale. but the result is different. I expected that 4, 6, 8 are the means for clusters. Did I do anything wrong here.
unit year y
1 1 2
1 2 4
1 3 6
2 1 3
2 2 6
2 3 9
3 1 4
3 2 8
3 3 12
xm_y
1:1 3
1:2 6
1:3 9
2:1 3
2:2 6
2:3 9
3:1 3
3:2 6
3:3 9
2 weeks, 2 days
