Entiendo que los resultados de los márgenes no deberían ser sensibles al periodo base elegido para una variable temporal categórica. Sin embargo, me parece que sí lo son.
use http://www.stata-press.com/data/r12/nlswork.dta, clear
set seed 1234
gen rndm=uniform()
bys idcode: egen x=mean(rndm)
gen treated_group=(x<0.4)
drop rndm x
qui areg ln_wage i.treated_group##ib68.year, absorb(idcode)
margins year, at (treated_group=(0 1)) noestimcheck
Adjusted predictions Number of obs = 28,534
Model VCE : OLS
Expression : Linear prediction, predict()
1._at : treated_group = 0
2._at : treated_group = 1
------------------------------------------------------------------------------
| Delta-method
| Margin Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
_at#year |
1 68 | 1.444114 .0092306 156.45 0.000 1.426021 1.462206
1 69 | 1.536417 .0110225 139.39 0.000 1.514812 1.558022
1 70 | 1.519892 .0096172 158.04 0.000 1.501042 1.538742
1 71 | 1.569712 .0092502 169.70 0.000 1.551581 1.587843
1 72 | 1.580554 .0095499 165.50 0.000 1.561835 1.599272
1 73 | 1.599594 .0089301 179.12 0.000 1.582091 1.617098
1 75 | 1.612995 .0086646 186.16 0.000 1.596012 1.629978
1 77 | 1.668559 .0086132 193.72 0.000 1.651676 1.685441
1 78 | 1.703676 .0089237 190.92 0.000 1.686185 1.721167
1 80 | 1.716041 .0091365 187.82 0.000 1.698132 1.733949
1 82 | 1.733376 .0087527 198.04 0.000 1.71622 1.750532
1 83 | 1.757594 .0089667 196.01 0.000 1.740018 1.775169
1 85 | 1.806221 .0088706 203.62 0.000 1.788834 1.823608
1 87 | 1.824522 .0087495 208.53 0.000 1.807372 1.841671
1 88 | 1.879086 .0087315 215.21 0.000 1.861972 1.8962
2 68 | 1.444114 .0092306 156.45 0.000 1.426021 1.462206
2 69 | 1.505828 .0289567 52.00 0.000 1.449071 1.562585
2 70 | 1.504917 .0267331 56.29 0.000 1.452518 1.557315
2 71 | 1.56406 .0266853 58.61 0.000 1.511755 1.616365
2 72 | 1.579169 .0277075 56.99 0.000 1.524861 1.633477
2 73 | 1.573991 .0269577 58.39 0.000 1.521152 1.626829
2 75 | 1.59415 .0267183 59.67 0.000 1.541781 1.64652
2 77 | 1.649944 .0268489 61.45 0.000 1.597319 1.70257
2 78 | 1.688432 .0277801 60.78 0.000 1.633981 1.742882
2 80 | 1.673237 .0282171 59.30 0.000 1.61793 1.728545
2 82 | 1.708132 .0273014 62.57 0.000 1.65462 1.761645
2 83 | 1.733978 .0277659 62.45 0.000 1.679555 1.788401
2 85 | 1.79532 .027219 65.96 0.000 1.741969 1.848671
2 87 | 1.805997 .0272753 66.21 0.000 1.752536 1.859458
2 88 | 1.882125 .0271057 69.44 0.000 1.828996 1.935254
------------------------------------------------------------------------------Nota: Estoy utilizando el año base 68. El valor medio previsto para el grupo tratado en el momento 88 es de 1,879.
Ahora, no cambie nada más que el año base, a 69:
qui areg ln_wage i.treated_group##ib69.year, absorb(idcode)
margins year, at (treated_group=(0 1)) noestimcheck
Adjusted predictions Number of obs = 28,534
Model VCE : OLS
Expression : Linear prediction, predict()
1._at : treated_group = 0
2._at : treated_group = 1
------------------------------------------------------------------------------
| Delta-method
| Margin Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
_at#year |
1 68 | 1.43873 .0109392 131.52 0.000 1.417288 1.460171
1 69 | 1.531033 .0093871 163.10 0.000 1.512634 1.549433
1 70 | 1.514508 .0096481 156.97 0.000 1.495597 1.533419
1 71 | 1.564328 .0092902 168.38 0.000 1.546119 1.582538
1 72 | 1.57517 .0095905 164.24 0.000 1.556372 1.593968
1 73 | 1.594211 .0089738 177.65 0.000 1.576621 1.6118
1 75 | 1.607611 .0087209 184.34 0.000 1.590518 1.624705
1 77 | 1.663175 .0086695 191.84 0.000 1.646182 1.680168
1 78 | 1.698292 .0089774 189.17 0.000 1.680696 1.715888
1 80 | 1.710657 .0091904 186.14 0.000 1.692643 1.728671
1 82 | 1.727992 .0088108 196.12 0.000 1.710723 1.745262
1 83 | 1.75221 .009026 194.13 0.000 1.734518 1.769901
1 85 | 1.800837 .0089313 201.63 0.000 1.783331 1.818343
1 87 | 1.819138 .0088103 206.48 0.000 1.80187 1.836407
1 88 | 1.873702 .0087973 212.98 0.000 1.856459 1.890945
2 68 | 1.469319 .0288182 50.99 0.000 1.412834 1.525805
2 69 | 1.531033 .0093871 163.10 0.000 1.512634 1.549433
2 70 | 1.530122 .0268025 57.09 0.000 1.477588 1.582657
2 71 | 1.589266 .0268825 59.12 0.000 1.536575 1.641957
2 72 | 1.604375 .0279935 57.31 0.000 1.549506 1.659244
2 73 | 1.599196 .0273319 58.51 0.000 1.545624 1.652769
2 75 | 1.619356 .0271298 59.69 0.000 1.56618 1.672532
2 77 | 1.67515 .0272555 61.46 0.000 1.621727 1.728572
2 78 | 1.713637 .0281276 60.92 0.000 1.658505 1.768769
2 80 | 1.698443 .0285348 59.52 0.000 1.642513 1.754373
2 82 | 1.733338 .0276316 62.73 0.000 1.679178 1.787497
2 83 | 1.759184 .0280852 62.64 0.000 1.704135 1.814232
2 85 | 1.820525 .0275764 66.02 0.000 1.766474 1.874577
2 87 | 1.831203 .027657 66.21 0.000 1.776993 1.885412
2 88 | 1.907331 .0274802 69.41 0.000 1.853468 1.961194
------------------------------------------------------------------------------Ahora, en lugar de 1,879, el valor medio previsto para el grupo tratado en el tiempo 88 es de 1,907.
Dado que los valores predichos son sensibles a un cambio en el periodo de tiempo base, ¿cómo pueden interpretarse los resultados de los márgenes?
