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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ab02*| -.0132912 .01201 -1.11 0.269 -.036837 .010254 .034814<br />
ab36*| .0080002 .00406 1.97 0.049 .000038 .015962 .072629<br />
ab79*| .0064272 .0047 1.37 0.171 -.002775 .015629 .083433<br />
ab1014*| .0005212 .00646 0.08 0.936 -.012143 .013186 .159664<br />
single*| -.005143 .01093 -0.47 0.638 -.026564 .016278 .233493<br />
iel<strong>and</strong>1*| .1293632 .09316 1.39 0.165 -.053225 .311951 .972989<br />
iel<strong>and</strong>2*| .0102742 .00383 2.69 0.007 .002775 .017774 .012605<br />
short*| -.002228 .00795 -0.28 0.779 -.017806 .01335 .418968<br />
higher*| .0086015 .00418 2.06 0.040 .000405 .016798 .210684<br />
agesq | .0000395 .00002 2.51 0.012 8.6e-06 .00007 2240.74<br />
use<strong>of</strong>m~n | .0000123 .00001 0.86 0.389 -.000016 .00004 620.683<br />
u | -.0012752 .00167 -0.76 0.446 -.004557 .002006 6.08998<br />
ohat | .0004623 .00043 1.08 0.282 -.00038 .001305 2.7e-08<br />
y96*| -.0105591 .01629 -0.65 0.517 -.042484 .021366 .060624<br />
y97*| -.0046915 .01229 -0.38 0.703 -.028775 .019392 .077431<br />
y98*| -.0085307 .02198 -0.39 0.698 -.051601 .03454 .086435<br />
y99*| -.0017555 .01095 -0.16 0.873 -.023214 .019702 .107443<br />
y00*| -.0178656 .023 -0.78 0.437 -.062947 .027216 .132653<br />
y01*| -.0173545 .0225 -0.77 0.441 -.061455 .026746 .136255<br />
y02*| -.0053231 .008 -0.67 0.506 -.020999 .010353 .160864<br />
------------------------------------------------------------------------------<br />
(*) dy/dx is for discrete change <strong>of</strong> dummy variable from 0 to 1<br />
R<strong>and</strong>om effects probit – Table 7:<br />
. use /akf/702517/ycb2517/Initial/finaldata4.dta<br />
. /*Panel probit <strong>on</strong> the full sample - same estimati<strong>on</strong> as Galarraga */<br />
. iis udtrnr<br />
. tis yr<br />
.<br />
. /*Test for sample selecti<strong>on</strong> */<br />
. xtprobit emp antidep mtx wageinc age ab02 ab36 ab79 ab1014 single iel<strong>and</strong>1 iel<br />
> <strong>and</strong>2 short higher agesq use<strong>of</strong>medicin u s_1<br />
Fitting comparis<strong>on</strong> model:<br />
Iterati<strong>on</strong> 0: log likelihood = -503.06464<br />
Iterati<strong>on</strong> 1: log likelihood = -328.50135<br />
Iterati<strong>on</strong> 2: log likelihood = -284.26838<br />
Iterati<strong>on</strong> 3: log likelihood = -273.9389<br />
Iterati<strong>on</strong> 4: log likelihood = -272.86409<br />
Iterati<strong>on</strong> 5: log likelihood = -272.84747<br />
Iterati<strong>on</strong> 6: log likelihood = -272.84746<br />
Fitting full model:<br />
rho = 0.0 log likelihood = -272.84746<br />
rho = 0.1 log likelihood = -263.18252<br />
rho = 0.2 log likelihood = -256.80355<br />
rho = 0.3 log likelihood = -253.12946<br />
rho = 0.4 log likelihood = -252.12012<br />
rho = 0.5 log likelihood = -254.14923<br />
Iterati<strong>on</strong> 0: log likelihood = -252.12012<br />
Iterati<strong>on</strong> 1: log likelihood = -236.43227<br />
Iterati<strong>on</strong> 2: log likelihood = -228.71939<br />
Iterati<strong>on</strong> 3: log likelihood = -225.43945<br />
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