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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age | -.0017802 .00166 -1.07 0.284 -.005038 .001477 46.7596<br />
ab02*| .0025922 .0028 0.92 0.355 -.002904 .008089 .048077<br />
ab36*| .0013762 .00185 0.74 0.457 -.00225 .005002 .089744<br />
ab1014*| .002218 .00243 0.91 0.361 -.002545 .006981 .179487<br />
single*| -.0012006 .00313 -0.38 0.702 -.007342 .004941 .211538<br />
iel<strong>and</strong>1*| .0331489 .04043 0.82 0.412 -.046101 .112399 .983974<br />
short*| .0028835 .00422 0.68 0.495 -.005393 .01116 .461538<br />
higher*| .0049005 .00484 1.01 0.311 -.004577 .014378 .214744<br />
agesq | .0000251 .00002 1.09 0.276 -.00002 .00007 2293.48<br />
use<strong>of</strong>m~n | -3.53e-06 .00000 -0.76 0.447 -.000013 5.6e-06 650.074<br />
u | -.0008144 .00135 -0.60 0.545 -.003453 .001825 5.99904<br />
ohat | -.0000368 .00006 -0.66 0.509 -.000146 .000072 2.1e-07<br />
------------------------------------------------------------------------------<br />
(*) dy/dx is for discrete change <strong>of</strong> dummy variable from 0 to 1<br />
Pooled probit – Table 7:<br />
. use /akf/702517/ycb2517/Initial/finaldata4.dta<br />
. /*EMP/UNEMP w dosis - pooled probit - robust std errors*/<br />
. probit emp antidep mtx wageinc age ab02 ab36 ab79 ab1014 single iel<strong>and</strong>1 ielan<br />
> d2 short higher agesq use<strong>of</strong>medicin u y96 y97 y98 y99 y00 y01 y02 y03, robust<br />
Iterati<strong>on</strong> 0: log pseudolikelihood = -520.18215<br />
Iterati<strong>on</strong> 1: log pseudolikelihood = -346.48503<br />
Iterati<strong>on</strong> 2: log pseudolikelihood = -303.98085<br />
Iterati<strong>on</strong> 3: log pseudolikelihood = -294.47534<br />
Iterati<strong>on</strong> 4: log pseudolikelihood = -293.55088<br />
Iterati<strong>on</strong> 5: log pseudolikelihood = -293.53868<br />
Iterati<strong>on</strong> 6: log pseudolikelihood = -293.53868<br />
Probit estimates Number <strong>of</strong> obs = 1666<br />
Wald chi2(24) = 210.95<br />
Prob > chi2 = 0.0000<br />
Log pseudolikelihood = -293.53868 Pseudo R2 = 0.4357<br />
------------------------------------------------------------------------------<br />
| Robust<br />
emp | Coef. Std. Err. z P>|z| [95% C<strong>on</strong>f. Interval]<br />
-------------+----------------------------------------------------------------<br />
antidep | -.0006571 .000375 -1.75 0.080 -.001392 .0000779<br />
mtx | .0002961 .0005364 0.55 0.581 -.0007553 .0013474<br />
wageinc | .0000108 9.10e-07 11.91 0.000 9.06e-06 .0000126<br />
age | -.1286814 .0336078 -3.83 0.000 -.1945515 -.0628113<br />
ab02 | -.27353 .2156843 -1.27 0.205 -.6962635 .1492035<br />
ab36 | .1753017 .173093 1.01 0.311 -.1639545 .5145578<br />
ab79 | .0590033 .1671881 0.35 0.724 -.2686794 .386686<br />
ab1014 | -.2142583 .1359539 -1.58 0.115 -.4807231 .0522065<br />
single | -.4974125 .1283413 -3.88 0.000 -.7489568 -.2458683<br />
iel<strong>and</strong>1 | .8901735 .245906 3.62 0.000 .4082066 1.37214<br />
iel<strong>and</strong>2 | 1.34741 .5449621 2.47 0.013 .2793043 2.415517<br />
short | .1836273 .1310009 1.40 0.161 -.0731298 .4403844<br />
higher | .4822753 .2152543 2.24 0.025 .0603846 .904166<br />
agesq | .0017561 .0004272 4.11 0.000 .0009188 .0025934<br />
use<strong>of</strong>medicin | -.0001153 .0001087 -1.06 0.289 -.0003284 .0000979<br />
u | -.0255393 .0468083 -0.55 0.585 -.1172819 .0662034<br />
y96 | -.3940673 .4162923 -0.95 0.344 -1.209985 .4218506<br />
y97 | -.22831 .4037479 -0.57 0.572 -1.019641 .5630214<br />
y98 | -.1415524 .433873 -0.33 0.744 -.9919278 .708823<br />
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