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 ...
You also want an ePaper? Increase the reach of your titles
YUMPU automatically turns print PDFs into web optimized ePapers that Google loves.
iel<strong>and</strong>1*| .2476048 .11071 2.24 0.025 .030622 .464587 .959814<br />
iel<strong>and</strong>2*| .2371787 .07029 3.37 0.001 .09942 .374938 .021997<br />
short*| .0255055 .04589 0.56 0.578 -.064436 .115447 .376904<br />
higher*| -.1266057 .06846 -1.85 0.064 -.260786 .007574 .158629<br />
agesq | .0001052 .00016 0.64 0.521 -.000216 .000427 2630.49<br />
use<strong>of</strong>m~n | .0000177 .00011 0.16 0.876 -.000205 .00024 870.992<br />
u | -.0398881 .0257 -1.55 0.121 -.090269 .010493 5.4599<br />
ohat | .0015705 .00145 1.09 0.278 -.001265 .004406 5.9e-08<br />
y99*| -.0366351 .08521 -0.43 0.667 -.203652 .130382 .15736<br />
y00*| -.0556498 .08314 -0.67 0.503 -.218599 .1073 .18824<br />
y01*| -.0611071 .07372 -0.83 0.407 -.205595 .083381 .192047<br />
y02*| -.0490084 .05604 -0.87 0.382 -.158844 .060828 .217005<br />
------------------------------------------------------------------------------<br />
(*) dy/dx is for discrete change <strong>of</strong> dummy variable from 0 to 1<br />
.<br />
. probit emp c<strong>on</strong>s_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 y96 y97 y98 y99 y00 y01 y02 y03, ro<br />
> bust<br />
note: y96 dropped due to collinearity<br />
note: y97 dropped due to collinearity<br />
note: y98 dropped due to collinearity<br />
note: y99 dropped due to collinearity<br />
Iterati<strong>on</strong> 0: log pseudolikelihood = -1632.6254<br />
Iterati<strong>on</strong> 1: log pseudolikelihood = -780.50444<br />
Iterati<strong>on</strong> 2: log pseudolikelihood = -591.88274<br />
Iterati<strong>on</strong> 3: log pseudolikelihood = -549.91224<br />
Iterati<strong>on</strong> 4: log pseudolikelihood = -544.2132<br />
Iterati<strong>on</strong> 5: log pseudolikelihood = -544.06193<br />
Iterati<strong>on</strong> 6: log pseudolikelihood = -544.06181<br />
Probit estimates Number <strong>of</strong> obs = 2364<br />
Wald chi2(20) = 544.92<br />
Prob > chi2 = 0.0000<br />
Log pseudolikelihood = -544.06181 Pseudo R2 = 0.6668<br />
------------------------------------------------------------------------------<br />
| Robust<br />
emp | Coef. Std. Err. z P>|z| [95% C<strong>on</strong>f. Interval]<br />
-------------+----------------------------------------------------------------<br />
c<strong>on</strong>s_antidep | -.0001251 .0001144 -1.09 0.274 -.0003493 .0000991<br />
mtx | .0003821 .0004056 0.94 0.346 -.000413 .0011771<br />
wageinc | .000017 8.61e-07 19.72 0.000 .0000153 .0000187<br />
age | -.0128591 .0242836 -0.53 0.596 -.060454 .0347358<br />
ab02 | .011787 .2195441 0.05 0.957 -.4185117 .4420856<br />
ab36 | -.1956624 .1803195 -1.09 0.278 -.549082 .1577572<br />
ab79 | -.1650562 .1539095 -1.07 0.284 -.4667133 .1366008<br />
ab1014 | -.1844969 .1332886 -1.38 0.166 -.4457378 .0767441<br />
single | -.2979809 .1012135 -2.94 0.003 -.4963557 -.0996061<br />
iel<strong>and</strong>1 | .4382736 .2257576 1.94 0.052 -.0042032 .8807503<br />
iel<strong>and</strong>2 | .7472589 .334164 2.24 0.025 .0923095 1.402208<br />
short | .1643443 .0892956 1.84 0.066 -.0106718 .3393603<br />
higher | -.3002649 .170399 -1.76 0.078 -.6342408 .0337111<br />
agesq | -.0001038 .0002728 -0.38 0.704 -.0006384 .0004308<br />
use<strong>of</strong>medicin | -.0002794 .0000674 -4.14 0.000 -.0004115 -.0001472<br />
u | -.0441862 .0358021 -1.23 0.217 -.1143571 .0259847<br />
y00 | -.0750326 .1312714 -0.57 0.568 -.3323198 .1822546<br />
y01 | -.1284746 .1379387 -0.93 0.352 -.3988295 .1418803<br />
138
Change language