Trabajo lohit
Enviado por Sara • 3 de Diciembre de 2018 • 6.412 Palabras (26 Páginas) • 289 Visitas
...
Ordered logistic regression Number of obs = 914
LR chi2(5) = 91.02
Prob > chi2 = 0.0000
Log likelihood = -1083.2871 Pseudo R2 = 0.0403
------------------------------------------------------------------------------
warm | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
male | -.7589487 .1265685 -6.00 0.000 -1.007018 -.510879
white | -.3579802 .18615 -1.92 0.054 -.7228275 .0068671
age | -.0227585 .0038285 -5.94 0.000 -.0302621 -.0152548
ed | .0421346 .025821 1.63 0.103 -.0084737 .0927429
prst | .0094713 .0049673 1.91 0.057 -.0002645 .019207
-------------+----------------------------------------------------------------
/cut1 | -3.528722 .4055 -4.323487 -2.733957
/cut2 | -1.380973 .3850552 -2.135668 -.6262787
/cut3 | .6297378 .3821996 -.1193596 1.378835
------------------------------------------------------------------------------
.
. * comparando ORDINAL LOGIT con ORDINAL PROBIT
. ologit warm yr89 male white age ed prst, nolog
Ordered logistic regression Number of obs = 2293
LR chi2(6) = 301.72
Prob > chi2 = 0.0000
Log likelihood = -2844.9123 Pseudo R2 = 0.0504
------------------------------------------------------------------------------
warm | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
yr89 | .5239025 .0798988 6.56 0.000 .3673037 .6805013
male | -.7332997 .0784827 -9.34 0.000 -.8871229 -.5794766
white | -.3911595 .1183808 -3.30 0.001 -.6231815 -.1591374
age | -.0216655 .0024683 -8.78 0.000 -.0265032 -.0168278
ed | .0671728 .015975 4.20 0.000 .0358624 .0984831
prst | .0060727 .0032929 1.84 0.065 -.0003813 .0125267
-------------+----------------------------------------------------------------
/cut1 | -2.465362 .2389126 -2.933622 -1.997102
/cut2 | -.630904 .2333155 -1.088194 -.173614
/cut3 | 1.261854 .2340179 .8031873 1.720521
------------------------------------------------------------------------------
. estimates store ologit // guarda los estimadores ologit
. oprobit warm yr89 male white age ed prst, nolog
Ordered probit regression Number of obs = 2293
LR chi2(6) = 294.32
Prob > chi2 = 0.0000
Log likelihood = -2848.611 Pseudo R2 = 0.0491
------------------------------------------------------------------------------
warm | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
yr89 | .3188147 .0468519 6.80 0.000 .2269867 .4106427
male | -.4170287 .0455459 -9.16 0.000 -.5062971 -.3277603
white | -.2265002 .0694773 -3.26 0.001 -.3626733 -.0903272
age | -.0122213 .0014427 -8.47 0.000 -.0150489 -.0093937
ed | .0387234 .0093241 4.15 0.000 .0204485 .0569982
prst | .003283 .001925 1.71 0.088 -.0004899 .0070559
-------------+----------------------------------------------------------------
/cut1 | -1.428578 .1387742 -1.700571 -1.156586
/cut2 | -.3605589 .1369219 -.6289209 -.092197
/cut3 | .7681637 .1370564 .4995381 1.036789
------------------------------------------------------------------------------
. estimates store oprobit // guarda los estimadores oprobit
. estimates table ologit oprobit, b(%9.3f) t label varwidth(30)
--------------------------------------------------------
Variable | ologit oprobit
-------------------------------+------------------------
warm |
Survey year: 1=1989 0=1977 | 0.524 0.319
| 6.56 6.80
Gender: 1=male 0=female | -0.733 -0.417
| -9.34 -9.16
Race: 1=white 0=not white | -0.391 -0.227
| -3.30 -3.26
Age in years | -0.022 -0.012
| -8.78 -8.47
Years of education | 0.067 0.039
| 4.20 4.15
Occupational prestige | 0.006 0.003
|
...