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.
------------------------------------------------------------------------------<br />
variable | dy/dx Std. Err. z P>|z| [ 95% C.I. ] X<br />
---------+--------------------------------------------------------------------<br />
antidep | -.0040303 .00289 -1.39 0.164 -.009701 .00164 36.3851<br />
mtx | .0005653 .00041 1.37 0.171 -.000244 .001375 220.258<br />
wageinc | 5.41e-06 .00000 13.83 0.000 4.6e-06 6.2e-06 115135<br />
age | -.0171922 .01522 -1.13 0.259 -.047026 .012641 49.9776<br />
ab36*| .033996 .0999 0.34 0.734 -.161795 .229787 .063793<br />
ab79*| .1617566 .07862 2.06 0.040 .007662 .315852 .067241<br />
ab1014*| .0264052 .0744 0.35 0.723 -.119409 .172219 .136207<br />
single*| .0443217 .07311 0.61 0.544 -.09898 .187623 .255172<br />
iel<strong>and</strong>1*| .268155 .21226 1.26 0.206 -.147859 .684169 .960345<br />
iel<strong>and</strong>2*| .20052 .0711 2.82 0.005 .061176 .339864 .018966<br />
short*| .0099763 .07529 0.13 0.895 -.137585 .157538 .4<br />
higher*| .0167037 .0638 0.26 0.793 -.108348 .141756 .174138<br />
agesq | .0000932 .00017 0.55 0.583 -.000239 .000426 2610.34<br />
use<strong>of</strong>m~n | .0000391 .00007 0.58 0.564 -.000094 .000172 866.898<br />
u | -.0608333 .03055 -1.99 0.046 -.120708 -.000959 6.07914<br />
ohat | .0040349 .00289 1.40 0.163 -.001628 .009697 -2.0e-07<br />
------------------------------------------------------------------------------<br />
(*) dy/dx is for discrete change <strong>of</strong> dummy variable from 0 to 1<br />
Pooled probit – Table 6:<br />
. use /akf/702517/ycb2517/Initial/finaldata2.dta<br />
.<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 = -2397.2264<br />
Iterati<strong>on</strong> 1: log pseudolikelihood = -1169.2049<br />
Iterati<strong>on</strong> 2: log pseudolikelihood = -904.39507<br />
Iterati<strong>on</strong> 3: log pseudolikelihood = -846.99784<br />
Iterati<strong>on</strong> 4: log pseudolikelihood = -839.93162<br />
Iterati<strong>on</strong> 5: log pseudolikelihood = -839.78242<br />
Iterati<strong>on</strong> 6: log pseudolikelihood = -839.78234<br />
Probit estimates Number <strong>of</strong> obs = 3508<br />
Wald chi2(24) = 763.38<br />
Prob > chi2 = 0.0000<br />
Log pseudolikelihood = -839.78234 Pseudo R2 = 0.6497<br />
------------------------------------------------------------------------------<br />
| Robust<br />
emp | Coef. Std. Err. z P>|z| [95% C<strong>on</strong>f. Interval]<br />
-------------+----------------------------------------------------------------<br />
antidep | -.0004748 .0002937 -1.62 0.106 -.0010504 .0001009<br />
mtx | .0001424 .0003617 0.39 0.694 -.0005666 .0008514<br />
wageinc | .0000168 7.23e-07 23.23 0.000 .0000154 .0000182<br />
age | -.0025277 .0186238 -0.14 0.892 -.0390298 .0339743<br />
ab02 | -.1657878 .2140784 -0.77 0.439 -.5853739 .2537982<br />
ab36 | -.319942 .1514898 -2.11 0.035 -.6168566 -.0230274<br />
ab79 | -.0667673 .129317 -0.52 0.606 -.3202239 .1866894<br />
ab1014 | -.2478912 .1069114 -2.32 0.020 -.4574338 -.0383487<br />
single | -.3209012 .0821024 -3.91 0.000 -.481819 -.1599834<br />
iel<strong>and</strong>1 | .6017234 .1915437 3.14 0.002 .2263047 .9771421<br />
iel<strong>and</strong>2 | .8218775 .2734025 3.01 0.003 .2860184 1.357737<br />
short | .1947805 .0729544 2.67 0.008 .0517924 .3377685<br />
higher | -.0391216 .127895 -0.31 0.760 -.2897912 .2115479<br />
108
Change language