April 2026 - Gretl-users - gretlml.univpm.it

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

3 days, 19 hours

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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 *

5 days

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Mixed command equivalent in gretl

by Ramki S

Hi, I am trying to replicate the results of an interesting paper by John antonakis. And they estimated a few models with mixed command of stata. Do we have ML estimator to replicate models 7 and 8. *7 MLE RE with Level 1 predictor, its cluster mean, and Level 2 predictor mixed y x x_cl z || lead_n: est store one *compare above estimate with below using likelihood ratio test mixed y x z || lead_n: est store two lrtest one two *8 ML RE with cluster-mean centered Level 1 predictor and Level 2 *predictor gen x_grp_cen = x - x_cl mixed y x_grp_cen x_cl z || lead_n: Datafile: https://www.dropbox.com/scl/fi/fqqenv2jlf5sxp1qe5a0o/antonakis_randomeffe... Paper: https://www.dropbox.com/scl/fi/zgqu5mv2doavbv66m3ako/antonakis2019_orm_ra... do file: https://www.dropbox.com/scl/fi/8aknnw6lg5qd6p425ox96/antonakis_random-eff...

1 week

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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, 2 days

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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, 3 days

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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 -------------------------------------------------------

1 week, 5 days

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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

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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

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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

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Time fixed effects option

by Ramki S

What is the gretl option for the following stata code? Table 14.1, Time effects. xtreg logc logq logf lf, i(t) fe https://stats.oarc.ucla.edu/stata/examples/greene/econometric-analysis-fo...

2 weeks

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Gretl-users April 2026