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 ...
Create successful ePaper yourself
Turn your PDF publications into a flip-book with our unique Google optimized e-Paper software.
u | -.060552 .0371395 -1.63 0.103 -.1333441 .0122401<br />
_c<strong>on</strong>s | -1.481301 1.573238 -0.94 0.346 -4.564792 1.602189<br />
-------------+----------------------------------------------------------------<br />
/lnsig2u | 1.427647 .1596375 1.114764 1.740531<br />
-------------+----------------------------------------------------------------<br />
sigma_u | 2.041783 .1629726 1.746095 2.387545<br />
rho | .8065345 .0249093 .7530161 .8507545<br />
------------------------------------------------------------------------------<br />
Likelihood-ratio test <strong>of</strong> rho=0: chibar2(01) = 438.09 Prob >= chibar2 = 0.000<br />
.<br />
. lrtest A .<br />
(log-likelihoods <strong>of</strong> null models cannot be compared)<br />
likelihood-ratio test LR chi2(1) = 0.02<br />
(Assumpti<strong>on</strong>: . nested in A) Prob > chi2 = 0.8808<br />
.<br />
. mfx<br />
Marginal effects after xtprobit<br />
y = Linear predicti<strong>on</strong> (predict)<br />
= .07922143<br />
------------------------------------------------------------------------------<br />
variable | dy/dx Std. Err. z P>|z| [ 95% C.I. ] X<br />
---------+--------------------------------------------------------------------<br />
ad_dummy*| -.3334316 .24473 -1.36 0.173 -.813098 .146235 .099772<br />
mtx | -.0005216 .00072 -0.73 0.468 -.001931 .000887 188.936<br />
wageinc | .0000341 .00000 15.05 0.000 .00003 .000039 88834.5<br />
age | .0399983 .05843 0.68 0.494 -.074519 .154516 49.9438<br />
ab02*| -.272244 .4634 -0.59 0.557 -1.1805 .636008 .022235<br />
ab36*| -.6195473 .36033 -1.72 0.086 -1.32577 .08668 .054162<br />
ab79*| -.2887016 .3452 -0.84 0.403 -.965278 .387875 .062999<br />
ab1014*| -.583789 .25151 -2.32 0.020 -1.07674 -.090843 .117161<br />
single*| -.7538968 .20697 -3.64 0.000 -1.15955 -.348248 .267674<br />
iel<strong>and</strong>1*| .8326754 .85438 0.97 0.330 -.84188 2.50723 .960946<br />
iel<strong>and</strong>2*| 1.475047 .94231 1.57 0.117 -.371839 3.32193 .023375<br />
short*| .6950118 .21122 3.29 0.001 .281022 1.109 .364025<br />
higher*| -.0771721 .28408 -0.27 0.786 -.633953 .479609 .151368<br />
agesq | -.0012727 .00064 -1.97 0.048 -.002536 -9.1e-06 2604.35<br />
use<strong>of</strong>m~n | -.0004527 .00011 -3.97 0.000 -.000676 -.000229 846.506<br />
u | -.060552 .03714 -1.63 0.103 -.133344 .01224 6.34359<br />
------------------------------------------------------------------------------<br />
(*) dy/dx is for discrete change <strong>of</strong> dummy variable from 0 to 1<br />
Probit yr 2003 – Table 6:<br />
. use /akf/702517/ycb2517/Initial/finaldata2.dta<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 if yr==2003, robust<br />
Iterati<strong>on</strong> 0: log pseudolikelihood = -402.02192<br />
Iterati<strong>on</strong> 1: log pseudolikelihood = -185.65116<br />
Iterati<strong>on</strong> 2: log pseudolikelihood = -129.61339<br />
Iterati<strong>on</strong> 3: log pseudolikelihood = -115.28655<br />
Iterati<strong>on</strong> 4: log pseudolikelihood = -112.34357<br />
Iterati<strong>on</strong> 5: log pseudolikelihood = -112.13527<br />
Iterati<strong>on</strong> 6: log pseudolikelihood = -112.13402<br />
105
Change language