I see. Well now I cannot think of much else to ask for :)). I thought
that the results with "stratified" bootstrap would work towards
producing smaller standard errors, but they did not seem to. They are
about the same as before.
One can argue that there is still an underlying quality of the
vehicle... and there is some room for measurement error with that
thing.
5% of 465 observations is 25... which is dangerously close to the 17
d.f.s you have. So perfect separation is a clear possibility for some
bootstrap samples.
On 12/8/08, Supnithadnaporn, Anupit <[email protected]> wrote:
> 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.
--
Stas Kolenikov, also found at http://stas.kolenikov.name
Small print: I use this email account for mailing lists only.
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