Dear all Statilist,

Andrew Musau , Richard Williams

I run the same model with two approaches , it suppose to give me the same results but in my analysis there are slightly differences , so from your opinion which model is better and why??
Code:
Augmented Mean Group estimator (Bond & Eberhardt, 2009; Eberhardt & Teal, 2010)

Common dynamic process included as additional regressor
All coefficients represent averages across groups (group variable: CountryID)
Coefficient averages computed as outlier-robust means (using rreg)

Mean Group type estimation                              Number of obs =    618
AMG                                                                    Wald chi2(8)  =  27.90
                                                                           Prob > chi2   = 0.0005

Y        Coefficient  Std. err.      z    P>z     [95% conf. interval]

Indep1_P   -.0115727   .0230853    -0.50   0.616    -.0568191    .0336737
Indep1_N     .022238   .0302148     0.74   0.462    -.0369818    .0814578
Indep2_P    .0401797    .026852     1.50   0.135    -.0124494    .0928087
Indep2_N   -.0263027   .0129571    -2.03   0.042    -.0516983   -.0009072
Indep3_P   -.0145813   .0174016    -0.84   0.402    -.0486879    .0195253
indep3_N    -.035766   .0368973    -0.97   0.332    -.1080835    .0365514
c_d_p        .1165165     .09198     1.27   0.205     -.063761    .2967939
trend         .0000876   .0002515     0.35   0.728    -.0004053    .0005805
_cons        -.030048   .0173433    -1.73   0.083    -.0640403    .0039444

Root Mean Squared Error (sigma): 0.0442
(RMSE uses residuals from group-specific regressions: unaffected by 'robust').
Variable c_d_p refers to the common dynamic process.
Variable trend refers to the group-specific linear trend terms.
Share of group-specific trends significant at 5% level: 0.091 (= 2 trends)
The second approach I use slope-dummy interaction terms

Code:
ugmented Mean Group estimator (Bond & Eberhardt, 2009; Eberhardt & Teal, 2010)

Common dynamic process included as additional regressor
All coefficients represent averages across groups (group variable: CountryID)
Coefficient averages computed as outlier-robust means (using rreg)

Mean Group type estimation                              Number of obs =    618
AMG                                                     Wald chi2(8)  =  28.00
                                                        Prob > chi2   = 0.0005



Y           Coefficient   Std. err.      z      P>z     [95% conf. interval]

Indep1          .022238   .0302148       0.74   0.462    -.0369818    .0814578
Indep2          -.0288838   .0126964    -2.27   0.023    -.0537682   -.0039993
Indep3           .0060371   .0138635     0.44   0.663    -.0211349    .0332091
inter_indep1P    -.0223807 .0506669    -0.44   0.659    -.1216859    .0769246
inter_indep2P      .0262421 .0178814     1.47   0.142    -.0088047    .0612889
inter_indep3P     -.0230171 .0289045    -0.80   0.426    -.0796689    .0336347
c_d_p              .1165165   .09198     1.27   0.205    -.0637609    .2967939
trend               .0000876   .0002515   0.35  0.728    -.0004053    .0005805
_cons             -.0048551    .009756   -0.50  0.619    -.0239765    .0142663

Root Mean Squared Error (sigma): 0.0442
(RMSE uses residuals from group-specific regressions: unaffected by 'robust').
Variable c_d_p refers to the common dynamic process.
Variable trend refers to the group-specific linear trend terms.
Share of group-specific trends significant at 5% level: 0.091 (= 2 trends)
So from the results and your opinion which one is better and dives efficient estimators and why?