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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ho = 0.2 log likelihood = -681.42709<br />
rho = 0.3 log likelihood = -663.68109<br />
rho = 0.4 log likelihood = -655.57901<br />
rho = 0.5 log likelihood = -655.97949<br />
Iterati<strong>on</strong> 0: log likelihood = -655.57902<br />
Iterati<strong>on</strong> 1: log likelihood = -601.13332<br />
Iterati<strong>on</strong> 2: log likelihood = -581.10382<br />
Iterati<strong>on</strong> 3: log likelihood = -575.20116<br />
Iterati<strong>on</strong> 4: log likelihood = -574.32505<br />
Iterati<strong>on</strong> 5: log likelihood = -574.29853<br />
Iterati<strong>on</strong> 6: log likelihood = -574.2985<br />
R<strong>and</strong>om-effects probit regressi<strong>on</strong> Number <strong>of</strong> obs = 3269<br />
Group variable (i): udtrnr Number <strong>of</strong> groups = 1133<br />
R<strong>and</strong>om effects u_i ~ Gaussian Obs per group: min = 1<br />
avg = 2.9<br />
max = 8<br />
Wald chi2(17) = 224.95<br />
Log likelihood = -574.2985 Prob > chi2 = 0.0000<br />
------------------------------------------------------------------------------<br />
emp | Coef. Std. Err. z P>|z| [95% C<strong>on</strong>f. Interval]<br />
-------------+----------------------------------------------------------------<br />
antidep | -.0007548 .0007805 -0.97 0.333 -.0022845 .0007749<br />
mtx | -.0005417 .0007807 -0.69 0.488 -.002072 .0009885<br />
wageinc | .000034 2.34e-06 14.55 0.000 .0000295 .0000386<br />
age | .0358341 .059697 0.60 0.548 -.0811699 .1528381<br />
ab02 | -.0665037 .4922627 -0.14 0.893 -1.031321 .8983135<br />
ab36 | -.5061335 .3765491 -1.34 0.179 -1.244156 .2318892<br />
ab79 | -.2245449 .3550032 -0.63 0.527 -.9203384 .4712485<br />
ab1014 | -.4837389 .2630451 -1.84 0.066 -.9992979 .0318201<br />
single | -.7490884 .2137255 -3.50 0.000 -1.167983 -.3301941<br />
iel<strong>and</strong>1 | .8667044 .911593 0.95 0.342 -.9199851 2.653394<br />
iel<strong>and</strong>2 | 1.570777 1.004257 1.56 0.118 -.3975299 3.539083<br />
short | .660297 .2222343 2.97 0.003 .2247257 1.095868<br />
higher | -.0635333 .3153708 -0.20 0.840 -.6816488 .5545821<br />
agesq | -.0011902 .0006702 -1.78 0.076 -.0025038 .0001233<br />
use<strong>of</strong>medicin | -.0004515 .0001186 -3.81 0.000 -.0006839 -.0002191<br />
u | -.075748 .0478583 -1.58 0.113 -.1695486 .0180525<br />
s_1 | -.0949006 .1626964 -0.58 0.560 -.4137797 .2239785<br />
_c<strong>on</strong>s | -1.396449 1.608461 -0.87 0.385 -4.548974 1.756077<br />
-------------+----------------------------------------------------------------<br />
/lnsig2u | 1.416024 .1677786 1.087184 1.744864<br />
-------------+----------------------------------------------------------------<br />
sigma_u | 2.029952 .1702912 1.722182 2.392723<br />
rho | .8047144 .0263663 .7478511 .8513039<br />
------------------------------------------------------------------------------<br />
Likelihood-ratio test <strong>of</strong> rho=0: chibar2(01) = 391.39 Prob >= chibar2 = 0.000<br />
.<br />
. /*with proxy, generalised residual*/<br />
. quietly xtprobit antidep mtx wageinc age ab02 ab36 ab79 ab1014 single iel<strong>and</strong>1<br />
> iel<strong>and</strong>2 short higher agesq use<strong>of</strong>medicin u<br />
.<br />
. predict xb, xb<br />
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