Pruebadeefectosindividuales

Prueba de efectos individuales
Yit= a+bxit+uit Efecto fijo (consideramos el componente no observado)
Yit=ai+bixit+uit Efecto aleatorio: No afectad
Supuesto: son diferentes para 4 individuos.
Hipotesis:
Hipotesis nula: Cada individuo tiene el mismo parámetro, si es asi se puede estimar por POLS, esto implica que justifica el panel
Hipotesis alternativa: tenemos que estimar ecuacionesindividuales.
Corremos: reg lgas lyp lprg lcar (logaritmo nos da elasticidad y largo plazo) EC1

forvalue id=1(1)18{ ECUACION RESTRINGIDA EC2
2. regress lgas lyp lprg lcar if id==`id'
3. }
Source | SS df MS Number of obs = 19
-------------+------------------------------ F( 3, 15) = 13.76
Model | .063401095 3 .021133698 Prob >F = 0.0001
Residual | .02304227 15 .001536151 R-squared = 0.7334
-------------+------------------------------ Adj R-squared = 0.6801
Total | .086443365 18 .004802409 Root MSE = .03919

------------------------------------------------------------------------------
lgas | Coef. Std. Err. t P>|t| [95%Conf. Interval]
-------------+----------------------------------------------------------------
lyp | .7607207 .2114705 3.60 0.003 .3099821 1.211459
lprg | -.7931991 .1500861 -5.28 0.000 -1.1131 -.4732982
lcar | -.5198709 .11313 -4.60 0.000 -.7610018 -.27874
_cons | 3.726605 .3730175 9.99 0.000 2.9315374.521673
------------------------------------------------------------------------------

Source | SS df MS Number of obs = 19
-------------+------------------------------ F( 3, 15) = 49.82
Model | .17486801 3 .058289337 Prob > F = 0.0000
Residual | .017549817 15 .001169988 R-squared =0.9088
-------------+------------------------------ Adj R-squared = 0.8906
Total | .192417826 18 .010689879 Root MSE = .03421

------------------------------------------------------------------------------
lgas | Coef. Std. Err. t P>|t| [95% Conf. Interval]-------------+----------------------------------------------------------------
lyp | .8450474 .170249 4.96 0.000 .4821703 1.207925
lprg | -.0416508 .1579112 -0.26 0.796 -.3782306 .294929
lcar | -.6734642 .0933163 -7.22 0.000 -.8723632 -.4745652
_cons | 3.041926 .4525118 6.72 0.000 2.07742 4.006432
------------------------------------------------------------------------------Source | SS df MS Number of obs = 19
-------------+------------------------------ F( 3, 15) = 23.73
Model | .010192796 3 .003397599 Prob > F = 0.0000
Residual | .002147859 15 .000143191 R-squared = 0.8260
-------------+------------------------------ Adj R-squared = 0.7911Total | .012340655 18 .000685592 Root MSE = .01197

------------------------------------------------------------------------------
lgas | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
lyp | .3924306 .0772591 5.08 0.000 .2277568 .5571044lprg | -.3629129 .0892983 -4.06 0.001 -.5532478 -.1725779
lcar | -.4385383 .0712286 -6.16 0.000 -.5903584 -.2867182
_cons | 3.125948 .2809957 11.12 0.000 2.52702 3.724876
------------------------------------------------------------------------------

Source | SS df MS Number of obs = 19...