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# st: xtprobit vs. probit

 From Davide Castellani <[email protected]> To [email protected] Subject st: xtprobit vs. probit Date Thu, 25 Jul 2002 13:00:11 +0200

Dear statalisters,
I am trying to run a random effect probit using -xtprobit, re-, but it yields the same results as the standard -probit- command. Furthermore, the xtprobit yields strange
output for lnsig2u, sigma_u and rho. The same does not happen if I use xtlogit. Below i report output for the xtprobit, probit, logit and xtlogit.
Do you have any idea of what could be going on?

Davide

. xtprobit dep x1 x2 if time<1998, re i(id)

Fitting comparison model:

Iteration 0: log likelihood = -1611.7984
Iteration 1: log likelihood = -1371.4379
Iteration 2: log likelihood = -1361.8071
Iteration 3: log likelihood = -1361.7691

Fitting full model:

rho = 0.0 log likelihood = -1361.7691
rho = 0.1 log likelihood = -1362.7407
Iteration 0: log likelihood = -1361.7691

Random-effects probit Number of obs = 9001
Group variable (i) : id Number of groups = 2602

Random effects u_i ~ Gaussian Obs per group: min = 1
avg = 3.5
max = 6

Wald chi2(2) = 467.84
Log likelihood = -1361.7691 Prob > chi2 = 0.0000

------------------------------------------------------------------------------
dep | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
x1 | .5067719 .0560958 9.03 0.000 .3968261 .6167178
x2 | .327031 .0186308 17.55 0.000 .2905153 .3635466
_cons | -3.31608 .0953378 -34.78 0.000 -3.502938 -3.129221
-------------+----------------------------------------------------------------
/lnsig2u | -14 . . .
-------------+----------------------------------------------------------------
sigma_u | .0009119 . . .
rho | 8.32e-07 . . .
------------------------------------------------------------------------------
Likelihood ratio test of rho=0: chibar2(01) = 0.00 Prob >= chibar2 = 1.000

. probit dep x1 x2 if time<1998

Iteration 0: log likelihood = -1611.7984
Iteration 1: log likelihood = -1371.4379
Iteration 2: log likelihood = -1361.8071
Iteration 3: log likelihood = -1361.7691

Probit estimates Number of obs = 9001
LR chi2(2) = 500.06
Prob > chi2 = 0.0000
Log likelihood = -1361.7691 Pseudo R2 = 0.1551

------------------------------------------------------------------------------
dep | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
x1 | .5067717 .0560951 9.03 0.000 .3968274 .616716
x2 | .3270309 .0186305 17.55 0.000 .2905158 .3635459
_cons | -3.316078 .0953354 -34.78 0.000 -3.502932 -3.129224
------------------------------------------------------------------------------

. logit dep x1 x2 if time<1998

Iteration 0: log likelihood = -1611.7984
Iteration 1: log likelihood = -1587.3111
Iteration 2: log likelihood = -1373.1369
Iteration 3: log likelihood = -1369.2397
Iteration 4: log likelihood = -1369.2142

Logit estimates Number of obs = 9001
LR chi2(2) = 485.17
Prob > chi2 = 0.0000
Log likelihood = -1369.2142 Pseudo R2 = 0.1505

------------------------------------------------------------------------------
dep | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
x1 | .9555873 .1115672 8.57 0.000 .7369195 1.174255
x2 | .6487152 .0359413 18.05 0.000 .5782716 .7191588
_cons | -6.320212 .1958625 -32.27 0.000 -6.704095 -5.936329
------------------------------------------------------------------------------

. xtlogit dep x1 x2 if time<1998, re i(id)

Fitting comparison model:

Iteration 0: log likelihood = -1611.7984
Iteration 1: log likelihood = -1587.3111
Iteration 2: log likelihood = -1373.1369
Iteration 3: log likelihood = -1369.2397
Iteration 4: log likelihood = -1369.2142

Fitting full model:

rho = 0.0 log likelihood = -1369.2142
rho = 0.1 log likelihood = -1367.2316
rho = 0.2 log likelihood = -1366.5762
rho = 0.3 log likelihood = -1368.2915
Iteration 0: log likelihood = -1366.5762
Iteration 1: log likelihood = -1363.6172
Iteration 2: log likelihood = -1362.5641
Iteration 3: log likelihood = -1362.5623
Iteration 4: log likelihood = -1362.5623

Random-effects logit Number of obs = 9001
Group variable (i) : id Number of groups = 2602

Random effects u_i ~ Gaussian Obs per group: min = 1
avg = 3.5
max = 6

Wald chi2(2) = 341.10
Log likelihood = -1362.5623 Prob > chi2 = 0.0000

------------------------------------------------------------------------------
dep | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
x1 | 1.040364 .1279023 8.13 0.000 .7896805 1.291048
x2 | .6961738 .0447027 15.57 0.000 .6085581 .7837896
_cons | -6.843286 .2759503 -24.80 0.000 -7.384139 -6.302434
-------------+----------------------------------------------------------------
/lnsig2u | -.4303919 .3243294 -1.066066 .2052821
-------------+----------------------------------------------------------------
sigma_u | .8063834 .1307669 .5868225 1.108094
rho | .165034 .0135847 .0947548 .2717889
------------------------------------------------------------------------------
Likelihood ratio test of rho=0: chibar2(01) = 13.30 Prob >= chibar2 = 0.000

.
end of do-file

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