Dear all,

I want to do regression across three sub-samples. However the result of regressing them separately vs. using interaction term is different. Could you please help? What did I do wrong? Thank you for your help.


Result by using separate regression for each sub-samples, if sector=1:

Code:
xtprobit over2 l.over2 c.l.lnGSCITOTSD##c.l.GSCITOTMG l.wgdp l.hgdp l.infl l.geopol if sector==1, re vce(robust)  


Random-effects probit regression                Number of obs     =      3,103
Group variable: firm                            Number of groups  =        107

Random effects u_i ~ Gaussian                   Obs per group:
                                                              min =         29
                                                              avg =       29.0
                                                              max =         29

Integration method: mvaghermite                 Integration pts.  =         12

                                                Wald chi2(8)      =     392.93
Log pseudolikelihood  = -1087.6732              Prob > chi2       =     0.0000

                                                (Std. Err. adjusted for 107 clusters in firm)
---------------------------------------------------------------------------------------------
                            |               Robust
                      over2 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------------+----------------------------------------------------------------
                      over2 |
                        L1. |   1.268935   .1114277    11.39   0.000     1.050541    1.487329
                            |
                lnGSCITOTSD |
                        L1. |  -.2170595   .0465004    -4.67   0.000    -.3081985   -.1259205
                            |
                  GSCITOTMG |
                        L1. |   .0275061   .0199155     1.38   0.167    -.0115275    .0665398
                            |
cL.lnGSCITOTSD#cL.GSCITOTMG |  -.0032262   .0033667    -0.96   0.338    -.0098247    .0033724
                            |
                       wgdp |
                        L1. |  -.0863806   .0282783    -3.05   0.002    -.1418052   -.0309561
                            |
                       hgdp |
                        L1. |   .2159382   .0230128     9.38   0.000      .170834    .2610424
                            |
                       infl |
                        L1. |   .0931856   .0088384    10.54   0.000     .0758627    .1105085
                            |
                     geopol |
                        L1. |   .0061763   .0016261     3.80   0.000     .0029892    .0093633
                            |
                      _cons |  -.8966739   .3538679    -2.53   0.011    -1.590242   -.2031056
----------------------------+----------------------------------------------------------------
                   /lnsig2u |  -.8778675   .2561907                     -1.379992    -.375743
----------------------------+----------------------------------------------------------------
                    sigma_u |   .6447235   .0825861                      .5015781    .8287212
                        rho |   .2936199   .0531358                      .2010103     .407154
---------------------------------------------------------------------------------------------

Result by using interaction term:

Code:
xtprobit over2 i.sector#(c.l.over2 c.l.lnGSCITOTSD##c.l.GSCITOTMG c.l.wgdp c.l.hgdp c.l.infl c.l.geopol), re vce(robust) 



Random-effects probit regression                Number of obs     =     14,732
Group variable: firm                            Number of groups  =        508

Random effects u_i ~ Gaussian                   Obs per group:
                                                              min =         29
                                                              avg =       29.0
                                                              max =         29

Integration method: mvaghermite                 Integration pts.  =         12

                                                Wald chi2(24)     =    1278.97
Log pseudolikelihood  = -4527.8125              Prob > chi2       =     0.0000

                                                       (Std. Err. adjusted for 508 clusters in firm)
----------------------------------------------------------------------------------------------------
                                   |               Robust
                             over2 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------------------------+----------------------------------------------------------------
                   sector#cL.over2 |
                                1  |   1.233693   .0995269    12.40   0.000     1.038624    1.428762
                                2  |   1.116174   .0856663    13.03   0.000     .9482715    1.284077
                                3  |   1.587107   .0876541    18.11   0.000     1.415308    1.758906
                                   |
             sector#cL.lnGSCITOTSD |
                                1  |   -.199594    .043342    -4.61   0.000    -.2845426   -.1146453
                                2  |  -.1114354   .0398321    -2.80   0.005    -.1895049   -.0333658
                                3  |  -.2216074    .043049    -5.15   0.000    -.3059818    -.137233
                                   |
               sector#cL.GSCITOTMG |
                                1  |   .0319543   .0200931     1.59   0.112    -.0074275    .0713361
                                2  |   .0587539   .0139361     4.22   0.000     .0314396    .0860682
                                3  |     .03742   .0156501     2.39   0.017     .0067464    .0680936
                                   |
sector#cL.lnGSCITOTSD#cL.GSCITOTMG |
                                1  |  -.0040029    .003388    -1.18   0.237    -.0106433    .0026376
                                2  |  -.0094008   .0023272    -4.04   0.000     -.013962   -.0048396
                                3  |  -.0052052   .0026101    -1.99   0.046     -.010321   -.0000894
                                   |
                    sector#cL.wgdp |
                                1  |  -.0853107   .0278813    -3.06   0.002    -.1399571   -.0306643
                                2  |  -.0565951   .0211258    -2.68   0.007    -.0980009   -.0151892
                                3  |  -.0896959   .0247988    -3.62   0.000    -.1383006   -.0410912
                                   |
                    sector#cL.hgdp |
                                1  |   .2314884   .0191855    12.07   0.000     .1938855    .2690913
                                2  |   .1925916   .0168027    11.46   0.000     .1596588    .2255243
                                3  |   .2353004   .0173111    13.59   0.000     .2013714    .2692295
                                   |
                    sector#cL.infl |
                                1  |   .0986052   .0077632    12.70   0.000     .0833896    .1138208
                                2  |   .0789797    .006462    12.22   0.000     .0663145    .0916449
                                3  |   .0970083   .0061669    15.73   0.000     .0849215    .1090951
                                   |
                  sector#cL.geopol |
                                1  |   .0064281   .0016887     3.81   0.000     .0031183    .0097378
                                2  |   .0054656   .0011434     4.78   0.000     .0032247    .0077066
                                3  |   .0091764    .001425     6.44   0.000     .0063835    .0119694
                                   |
                             _cons |  -1.090354   .1779324    -6.13   0.000    -1.439095    -.741613
-----------------------------------+----------------------------------------------------------------
                          /lnsig2u |  -.5038364   .0954541                     -.6909231   -.3167497
-----------------------------------+----------------------------------------------------------------
                           sigma_u |   .7773083   .0370987                      .7078936    .8535298
                               rho |   .3766395   .0224109                      .3338278    .4214681
----------------------------------------------------------------------------------------------------

Best regards,