An Analysis on Danish Micro Data - School of Economics and ...
An Analysis on Danish Micro Data - School of Economics and ...
An Analysis on Danish Micro Data - School of Economics and ...
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Iterati<strong>on</strong> 2: log likelihood = -306.65351<br />
Iterati<strong>on</strong> 3: log likelihood = -297.62904<br />
Iterati<strong>on</strong> 4: log likelihood = -296.8235<br />
Iterati<strong>on</strong> 5: log likelihood = -296.8144<br />
Iterati<strong>on</strong> 6: log likelihood = -296.8144<br />
Fitting full model:<br />
rho = 0.0 log likelihood = -296.8144<br />
rho = 0.1 log likelihood = -285.00709<br />
rho = 0.2 log likelihood = -277.10116<br />
rho = 0.3 log likelihood = -272.29709<br />
rho = 0.4 log likelihood = -270.46898<br />
rho = 0.5 log likelihood = -271.9719<br />
Iterati<strong>on</strong> 0: log likelihood = -270.46898<br />
Iterati<strong>on</strong> 1: log likelihood = -252.48048<br />
Iterati<strong>on</strong> 2: log likelihood = -243.50084<br />
Iterati<strong>on</strong> 3: log likelihood = -239.53855<br />
Iterati<strong>on</strong> 4: log likelihood = -238.22471<br />
Iterati<strong>on</strong> 5: log likelihood = -237.72186<br />
Iterati<strong>on</strong> 6: log likelihood = -237.65156<br />
Iterati<strong>on</strong> 7: log likelihood = -237.64545<br />
Iterati<strong>on</strong> 8: log likelihood = -237.64544<br />
R<strong>and</strong>om-effects probit regressi<strong>on</strong> Number <strong>of</strong> obs = 1666<br />
Group variable (i): udtrnr Number <strong>of</strong> groups = 675<br />
R<strong>and</strong>om effects u_i ~ Gaussian Obs per group: min = 1<br />
avg = 2.5<br />
max = 9<br />
Wald chi2(16) = 60.45<br />
Log likelihood = -237.64544 Prob > chi2 = 0.0000<br />
------------------------------------------------------------------------------<br />
emp | Coef. Std. Err. z P>|z| [95% C<strong>on</strong>f. Interval]<br />
-------------+----------------------------------------------------------------<br />
antidep | -.0012876 .00122 -1.06 0.291 -.0036788 .0011036<br />
mtx | .0002749 .0017379 0.16 0.874 -.0031312 .0036811<br />
wageinc | .0000303 4.54e-06 6.67 0.000 .0000214 .0000392<br />
age | -.3614512 .1306909 -2.77 0.006 -.6176006 -.1053017<br />
ab02 | -.4999006 .7842767 -0.64 0.524 -2.037055 1.037254<br />
ab36 | .4617282 .6015995 0.77 0.443 -.7173851 1.640842<br />
ab79 | -.1985345 .6540322 -0.30 0.761 -1.480414 1.083345<br />
ab1014 | -.1618629 .4460286 -0.36 0.717 -1.036063 .7123371<br />
single | -1.825653 .5585881 -3.27 0.001 -2.920465 -.73084<br />
iel<strong>and</strong>1 | 2.5429 1.576734 1.61 0.107 -.5474415 5.633242<br />
iel<strong>and</strong>2 | 3.71043 1.872427 1.98 0.048 .0405406 7.380319<br />
short | .8885039 .508357 1.75 0.080 -.1078575 1.884865<br />
higher | 2.062108 1.210824 1.70 0.089 -.3110637 4.435279<br />
agesq | .0047 .0016469 2.85 0.004 .001472 .0079279<br />
use<strong>of</strong>medicin | -.0000217 .0002111 -0.10 0.918 -.0004354 .000392<br />
u | .0459402 .0945739 0.49 0.627 -.1394212 .2313016<br />
_c<strong>on</strong>s | 4.281177 2.712866 1.58 0.115 -1.035942 9.598296<br />
-------------+----------------------------------------------------------------<br />
/lnsig2u | 2.361792 .4632415 1.453856 3.269729<br />
-------------+----------------------------------------------------------------<br />
sigma_u | 3.257292 .7544565 2.068715 5.128763<br />
rho | .913867 .0364636 .8105911 .9633756<br />
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