Re: st: clustering in quantile regressions with sampling weights

[Nikhil Srivastava <nikhil.del85@gmail.com>]

Thanks a lot for your advice, Stas. However I am a bit confused by the
statement that there are different commands to handle weighted data.Do
we have different commands to handle weighted data in bootstrap
itself?Could you please advice if the following procedure is correct?

webuse nhanes2
program bootit
qreg bpsystol height weight [aweight=finalwgt]
end
bootstrap "bootit" _b, cluster(psu) reps(500)

Thanks
Nikhil




On Sun, Oct 24, 2010 at 7:57 PM, Stas Kolenikov <skolenik@gmail.com> wrote:
> Yes, indeed, the bootstrap in its original form becomes unwieldy if
> you try to work it into the weighted data. That's why there are
> different commands to handle the weighted situation. They have limited
> functionality from the perspective of the classical bootstrap
> regarding features that don't make sense outside of i.i.d. situation
> (e.g., no BCa corrections).
>
> On Sun, Oct 24, 2010 at 9:00 PM, Nikhil Srivastava
> <nikhil.del85@gmail.com> wrote:
>> Thanks everybody for your help. As I do not have the variables that
>> were used to construct the sampling weights I ca not use them as
>> predictors in my model. The only reason I was worried about using
>> bootstrapping to get standard errors for my quantile regressions is
>> that the Stata Manual warns against using bootstarp with weighted
>> calculations-"bootstrap is not meant to be used with weighted
>> calculations"- Page-209, Volume [R], Stata Manual. I was a bit
>> apprehensive if this warning applies in my case too.Thanks
>>
>> Best,
>> Nikhil
>>
>> On Fri, Oct 22, 2010 at 9:31 AM, Stas Kolenikov <skolenik@gmail.com> wrote:
>>> Michael refers to the likelihood-based approach to survey data
>>> analysis, in which conditioning on the design variables makes the
>>> probability weights superfluous (note that conditioning is a very
>>> vague term; adding the design variables to the regression may not
>>> suffice, as you'd have to add their powers and interactions for an
>>> approximation to effective conditioning). Unfortunately, I don't think
>>> this will work out with quantile regression, as this is not a
>>> likelihood-based method. In my experience, the bootstrap methods seem
>>> to work well, although formal proofs are convoluted and rely on rather
>>> complicated conditions on the designs and sampling probabilities, and
>>> small sample performance is basically unknown (Austin and I are
>>> running some simulations to try to identify the limits of
>>> applicability, but we don't have any particularly strong findings yet
>>> to report). See my description of the survey bootstrap in SJ 10 (3)
>>> and accompanying package.
>>>
>>> On Fri, Oct 22, 2010 at 12:44 AM, Michael N. Mitchell wrote:
>>>>  I know this is a longshot, but do you know and have the variables involved
>>>> in creating the sampling weights (e.g., gender, age, race, etc...). If you
>>>> have the variables that were used to create the sampling weights, then you
>>>> could include those variables as predictors in your model to account for
>>>> them.
>>>
>>> --
>>> Stas Kolenikov, also found at http://stas.kolenikov.name
>>> Small print: I use this email account for mailing lists only.
>>>
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>>
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>
>
>
> --
> Stas Kolenikov, also found at http://stas.kolenikov.name
> Small print: I use this email account for mailing lists only.
>
> *
> *   For searches and help try:
> *   http://www.stata.com/help.cgi?search
> *   http://www.stata.com/support/statalist/faq
> *   http://www.ats.ucla.edu/stat/stata/
>

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