Hello!

I'd like to know why when I use multilevel models the estimates in of sample are differents of estimates out of sample, even when I use the same database.

When I make this procedure using logit I dont have problem, in other words, the estimates in or out of sample are the same.
Thanks

Code:
. melogit turismo idade filhos || pais:, nolog

Mixed-effects logistic regression               Number of obs     =      1,622
Group variable:            pais                 Number of groups  =         50

                                                Obs per group:
                                                              min =          2
                                                              avg =       32.4
                                                              max =        118

Integration method: mvaghermite                 Integration pts.  =          7

                                                Wald chi2(2)      =      52.18
Log likelihood = -1038.1176                     Prob > chi2       =     0.0000
------------------------------------------------------------------------------
     turismo |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       idade |   .0150543   .0066673     2.26   0.024     .0019866    .0281221
      filhos |  -.4239421   .0598524    -7.08   0.000    -.5412506   -.3066335
       _cons |   .4393717   .2954911     1.49   0.137    -.1397803    1.018524
-------------+----------------------------------------------------------------
pais         |
   var(_cons)|   .2551942   .0880849                      .1297368    .5019708
------------------------------------------------------------------------------
LR test vs. logistic model: chibar2(01) = 52.82       Prob >= chibar2 = 0.0000

. predict phat
(predictions based on fixed effects and posterior means of random effects)
(option mu assumed)
(using 7 quadrature points)

. preserve

. replace turismo = .
(1,622 real changes made, 1,622 to missing)

. predict phat2
(predictions based on fixed effects and posterior means of random effects)
(option mu assumed)
(using 7 quadrature points)

. list phat phat2 if pais=="Brasil"

      +---------------------+
      |     phat      phat2 |
      |---------------------|
1198. | .6316937   .6069548 |
1199. |  .491252   .4650681 |
1200. | .7533196   .7333011 |
1201. |  .747682   .7273715 |
1202. | .4950149   .4688152 |
      |---------------------|
1203. |  .491252   .4650681 |
1204. | .4874901   .4613249 |
1205. |  .717749   .6960087 |
1206. | .6659743   .6422338 |
1207. | .6068546   .5815524 |
      |---------------------|
1208. | .6068546   .5815524 |
1209. | .6032571   .5778845 |
1210. | .6175761   .5925006 |
1211. | .6495774   .6253273 |
1212. | .6731711   .6496731 |
      |---------------------|
1213. | .7207888   .6991845 |
1214. | .6862789   .6632519 |
      +---------------------+

. restore

. logit turismo idade filhos, nolog

Logistic regression                             Number of obs     =      1,622
                                                LR chi2(2)        =      49.40
                                                Prob > chi2       =     0.0000
Log likelihood = -1064.5279                     Pseudo R2         =     0.0227

------------------------------------------------------------------------------
     turismo |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       idade |   .0150046   .0063677     2.36   0.018     .0025241    .0274851
      filhos |  -.3869703    .056669    -6.83   0.000    -.4980395   -.2759011
       _cons |   .2924819   .2717957     1.08   0.282    -.2402278    .8251917
------------------------------------------------------------------------------

. predict phat3
(option pr assumed; Pr(turismo))

. replace turismo = .
(1,622 real changes made, 1,622 to missing)

. predict phat4
(option pr assumed; Pr(turismo))

. list phat3 phat4 if pais=="Brasil"

      +---------------------+
      |    phat3      phat4 |
      |---------------------|
1198. | .5887493   .5887493 |
1199. | .4555629   .4555629 |
1200. | .7032152   .7032152 |
1201. | .6969143   .6969143 |
1202. | .4592868   .4592868 |
      |---------------------|
1203. | .4555629   .4555629 |
1204. | .4518439   .4518439 |
1205. | .6716723   .6716723 |
1206. | .6245352   .6245352 |
1207. | .5631031   .5631031 |
      |---------------------|
1208. | .5631031   .5631031 |
1209. | .5594082   .5594082 |
1210. |  .574144    .574144 |
1211. | .5988468   .5988468 |
1212. | .6237966   .6237966 |
      |---------------------|
1213. | .6749727   .6749727 |
1214. | .6377733   .6377733 |
      +---------------------+

.
Code: