I really appreciate Stas for your elaborative explanation.
My data and model are simpler than your speculation.
Pr(FailEmissionTest) = f(OwnerCharacteristics,VehicleCharacteristics)
Owner characteristics = {Income, Race}
Vehicle characteristics = {Age,Mileage,Type(car,van,truck),ProductionCountry,
OtherEmissionControlTechnologies}
tresimp = First test result (1=Fail, 0=Pass)
indlninc = Ln of Individual household income
Vehicle emission test is legally required in some places in order to control
for pollution. However, this might cause more burden to the poor because
they tend to own old vehicles (I control for vehicle age as well).
In my data, only around 5.37% fail the first emission test.
As you suggested, ...
Thank you
Anupit
. bootstrap _b _se, reps(10000) strata(tresimp) saving("C:\ANUPIT\e1outACIlogitBOOT2Abse
> S.dta", replace): ///
> logit tresimp indlninc `owner' `vehicle', robust
(running logit on estimation sample)
Logistic regression
Number of strata = 2 Number of obs = 465
Replications = 5070
------------------------------------------------------------------------------
| Observed Bootstrap Normal-based
| Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
b |
indlninc | -.2818828 .491087 -0.57 0.566 -1.244396 .68063
black | 1.184949 .6017671 1.97 0.049 .0055069 2.36439
other | .5112052 .7492401 0.68 0.495 -.9572784 1.979689
age | .2291411 .088497 2.59 0.010 .0556902 .4025921
lnodo | .2668319 .5443578 0.49 0.624 -.8000898 1.333754
peuro | .3441804 .9920844 0.35 0.729 -1.600269 2.28863
pasia | .1141557 .7469322 0.15 0.879 -1.349805 1.578116
pother | .1774283 .7320229 0.24 0.808 -1.25731 1.612167
displ | -.0444871 .3925379 -0.11 0.910 -.8138473 .724873
indefi | .3926529 .808736 0.49 0.627 -1.192441 1.977746
indfi | -.5801308 .8902797 -0.65 0.515 -2.325047 1.164785
indmfi | -1.667607 .7216526 -2.31 0.021 -3.08202 -.2531942
egr1 | -1.307301 .609217 -2.15 0.032 -2.501345 -.1132581
tac1 | -.1766159 .9885491 -0.18 0.858 -2.114137 1.760905
car | -1.189119 1.641974 -0.72 0.469 -4.407329 2.029092
van | -1.120382 1.712288 -0.65 0.513 -4.476406 2.235641
_cons | -3.180481 9.098116 -0.35 0.727 -21.01246 14.6515
-------------+----------------------------------------------------------------
se |
indlninc | .3751495 .1107681 3.39 0.001 .158048 .5922511
black | .5377806 .1017104 5.29 0.000 .3384319 .7371293
other | .6130217 .1557758 3.94 0.000 .3077068 .9183367
age | .0683818 .0153066 4.47 0.000 .0383814 .0983822
lnodo | .3736896 .1499676 2.49 0.013 .0797586 .6676207
peuro | .8376832 .2292782 3.65 0.000 .3883061 1.28706
pasia | .6548303 .1297987 5.04 0.000 .4004296 .9092311
pother | .7830163 .2208942 3.54 0.000 .3500715 1.215961
displ | .3161949 .082542 3.83 0.000 .1544155 .4779743
indefi | .6165853 .1236292 4.99 0.000 .3742765 .858894
indfi | .6734173 .1310668 5.14 0.000 .4165312 .9303034
indmfi | 1.093033 .2006469 5.45 0.000 .699772 1.486293
egr1 | .5173223 .0856415 6.04 0.000 .3494679 .6851766
tac1 | .7599367 .1663593 4.57 0.000 .4338784 1.085995
car | .8430763 .3607768 2.34 0.019 .1359668 1.550186
van | .9862894 .3964712 2.49 0.013 .2092202 1.763359
_cons | 6.637172 1.948067 3.41 0.001 2.819031 10.45531
------------------------------------------------------------------------------
Note: One or more parameters could not be estimated in 4930 bootstrap replicates;
standard error estimates include only complete replications.
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
age | 465 6.926882 3.313216 3 20
lnodo | 465 11.41426 .5641127 8.209309 12.9082
black | 465 .255914 .4368437 0 1
other | 465 .144086 .3515552 0 1
indefi | 465 .2129032 .4098007 0 1
-------------+--------------------------------------------------------
indfi | 465 .2236559 .4171428 0 1
indmfi | 465 .2903226 .4544001 0 1
car | 465 .7225806 .4482074 0 1
van | 465 .1849462 .3886721 0 1
peuro | 465 .0666667 .2497125 0 1
-------------+--------------------------------------------------------
pasia | 465 .2430108 .4293635 0 1
pother | 465 .1204301 .3258143 0 1
indlninc | 465 10.82838 .5955187 8.517183 11.51293
tresimp | 465 .0537634 .2257932 0 1
egr1 | 465 .7591398 .4280662 0 1
-------------+--------------------------------------------------------
tac1 | 465 .0688172 .2534157 0 1
displ | 465 2.936825 1.05423 1 5.9
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