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> 7: log pseudolikelihood = -112.13402<br />
Probit estimates Number <strong>of</strong> obs = 580<br />
Wald chi2(16) = 162.84<br />
Prob > chi2 = 0.0000<br />
Log pseudolikelihood = -112.13402 Pseudo R2 = 0.7211<br />
------------------------------------------------------------------------------<br />
| Robust<br />
emp | Coef. Std. Err. z P>|z| [95% C<strong>on</strong>f. Interval]<br />
-------------+----------------------------------------------------------------<br />
antidep | .0000154 .0004548 0.03 0.973 -.0008761 .0009069<br />
mtx | .0006254 .0008781 0.71 0.476 -.0010955 .0023464<br />
wageinc | .0000186 1.75e-06 10.60 0.000 .0000152 .000022<br />
age | -.047387 .0504822 -0.94 0.348 -.1463303 .0515562<br />
ab02 | .5859691 .413446 1.42 0.156 -.2243701 1.396308<br />
ab36 | -.0973807 .3500935 -0.28 0.781 -.7835514 .58879<br />
ab79 | .2531098 .2983185 0.85 0.396 -.3315836 .8378033<br />
ab1014 | .1079526 .2600337 0.42 0.678 -.4017041 .6176094<br />
single | -.0607934 .2109381 -0.29 0.773 -.4742245 .3526377<br />
iel<strong>and</strong>1 | .3735862 .4498378 0.83 0.406 -.5080797 1.255252<br />
iel<strong>and</strong>2 | .8891427 .7057787 1.26 0.208 -.494158 2.272443<br />
short | .1833352 .2097941 0.87 0.382 -.2278536 .594524<br />
higher | .015583 .2155554 0.07 0.942 -.4068978 .4380639<br />
agesq | .0003117 .0005667 0.55 0.582 -.0007989 .0014223<br />
use<strong>of</strong>medicin | -.0001434 .0001076 -1.33 0.183 -.0003543 .0000676<br />
u | -.1062945 .0673309 -1.58 0.114 -.2382606 .0256717<br />
_c<strong>on</strong>s | .3145346 1.271094 0.25 0.805 -2.176764 2.805833<br />
------------------------------------------------------------------------------<br />
note: 0 failures <strong>and</strong> 14 successes completely determined.<br />
.<br />
. mfx<br />
Marginal effects after probit<br />
y = Pr(emp) (predict)<br />
= .77313128<br />
------------------------------------------------------------------------------<br />
variable | dy/dx Std. Err. z P>|z| [ 95% C.I. ] X<br />
---------+--------------------------------------------------------------------<br />
antidep | 4.65e-06 .00014 0.03 0.973 -.000264 .000273 36.3851<br />
mtx | .0001885 .00027 0.71 0.479 -.000334 .000711 220.258<br />
wageinc | 5.60e-06 .00000 14.36 0.000 4.8e-06 6.4e-06 115135<br />
age | -.0142786 .01511 -0.94 0.345 -.043901 .015344 49.9776<br />
ab02*| .1384632 .07478 1.85 0.064 -.008107 .285033 .031034<br />
ab36*| -.0302581 .11188 -0.27 0.787 -.249541 .189024 .063793<br />
ab79*| .0697885 .07543 0.93 0.355 -.078055 .217632 .067241<br />
ab1014*| .0315548 .07374 0.43 0.669 -.112965 .176075 .136207<br />
single*| -.0185202 .06508 -0.28 0.776 -.146069 .109029 .255172<br />
iel<strong>and</strong>1*| .1256807 .1661 0.76 0.449 -.199878 .451239 .960345<br />
iel<strong>and</strong>2*| .1795243 .08684 2.07 0.039 .009318 .349731 .018966<br />
short*| .0544482 .06223 0.88 0.382 -.067513 .176409 .4<br />
higher*| .0046775 .06458 0.07 0.942 -.121892 .131247 .174138<br />
agesq | .0000939 .00017 0.55 0.580 -.000239 .000426 2610.34<br />
use<strong>of</strong>m~n | -.0000432 .00003 -1.26 0.209 -.000111 .000024 866.898<br />
u | -.0320285 .02088 -1.53 0.125 -.072958 .008901 6.07914<br />
------------------------------------------------------------------------------<br />
(*) dy/dx is for discrete change <strong>of</strong> dummy variable from 0 to 1<br />
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