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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. /*with proxy, generalised residual*/<br />
. quietly xtprobit ad_dummy mtx wageinc age ab02 ab36 ab79 ab1014 single iel<strong>and</strong><br />
> 1 iel<strong>and</strong>2 short higher agesq use<strong>of</strong>medicin u<br />
.<br />
. predict xb, xb<br />
. gen normxb=norm(xb)<br />
. gen normdenxb=normden(xb)<br />
. gen denominator=normxb*[1-normxb]<br />
. gen numerator=normdenxb*[ad_dummy-normxb]<br />
. gen res=numerator/denominator<br />
.<br />
. xtprobit emp ad_dummy mtx wageinc age ab02 ab36 ab79 ab1014 single iel<strong>and</strong>1 ie<br />
> l<strong>and</strong>2 short higher agesq use<strong>of</strong>medicin u res<br />
Fitting comparis<strong>on</strong> model:<br />
Iterati<strong>on</strong> 0: log likelihood = -2397.2264<br />
Iterati<strong>on</strong> 1: log likelihood = -1170.2091<br />
Iterati<strong>on</strong> 2: log likelihood = -906.25634<br />
Iterati<strong>on</strong> 3: log likelihood = -849.1573<br />
Iterati<strong>on</strong> 4: log likelihood = -842.22476<br />
Iterati<strong>on</strong> 5: log likelihood = -842.08459<br />
Iterati<strong>on</strong> 6: log likelihood = -842.08453<br />
Fitting full model:<br />
rho = 0.0 log likelihood = -842.08451<br />
rho = 0.1 log likelihood = -775.97807<br />
rho = 0.2 log likelihood = -740.37837<br />
rho = 0.3 log likelihood = -720.29173<br />
rho = 0.4 log likelihood = -710.98419<br />
rho = 0.5 log likelihood = -711.06979<br />
Iterati<strong>on</strong> 0: log likelihood = -710.9842<br />
Iterati<strong>on</strong> 1: log likelihood = -652.81059<br />
Iterati<strong>on</strong> 2: log likelihood = -631.30507<br />
Iterati<strong>on</strong> 3: log likelihood = -624.69828<br />
Iterati<strong>on</strong> 4: log likelihood = -623.68727<br />
Iterati<strong>on</strong> 5: log likelihood = -623.66033<br />
Iterati<strong>on</strong> 6: log likelihood = -623.66031<br />
R<strong>and</strong>om-effects probit regressi<strong>on</strong> Number <strong>of</strong> obs = 3508<br />
Group variable (i): udtrnr Number <strong>of</strong> groups = 1202<br />
R<strong>and</strong>om effects u_i ~ Gaussian Obs per group: min = 1<br />
avg = 2.9<br />
max = 9<br />
Wald chi2(17) = 243.11<br />
Log likelihood = -623.66031 Prob > chi2 = 0.0000<br />
------------------------------------------------------------------------------<br />
emp | Coef. Std. Err. z P>|z| [95% C<strong>on</strong>f. Interval]<br />
102