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Re: st: Bootstrapping, Robust and Weight options in regress


From   Danny Dan <danny2011dan@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Bootstrapping, Robust and Weight options in regress
Date   Thu, 16 Feb 2012 23:55:04 -0600

Dear Stas,

Thank you so much for your answer. It would be really helpful if you
could guide me further in solving the issue.........

About using bootstrap:

I am actually using Coarsened Exact Matching (CEM using -cem- program)
in order to match my data. In CEM weights are generated after the
matching and I am using those weights in my regression analysis, where
I want to use bootstrap for standard errors. Do you think here I can
safely use -bootstrap- in the regression equation or still there will
be problem with sample variability accountability, and therefore, I
will have to write my own program? If the later is true then would it
be possible to guide me further into writing the program?

Please let me know.

I appreciate your help.

Thank you,

Dan


On Thu, Feb 16, 2012 at 9:38 PM, Stas Kolenikov <skolenik@gmail.com> wrote:
> First, in its simplest form (as implemented in -bootstrap- command),
> the bootstrap method assumes i.i.d. data. Weights of whatever flavor
> mean that data are not i.i.d. (heteroskedastic with aweights, sampled
> with differential probabilities with pweights), and you need to modify
> your bootstrap accordingly.
>
> Second, if you get your weights from a matching procedure (or any
> other input into the regression is obtained via some sort of
> estimation-prediction procedure), you have to bootstrap the whole
> process rather than its final stage, the regression. In Stata terms,
> you need to write your own little -program- that (i) accepts
> [pweights] as an input, (ii) does matching, (iii) produces weights,
> and (iv) feeds them into regression. Otherwise, your standard errors
> will be too small, and won't account for sampling variability in the
> intermediate statistics (such as, in your case, weights).
>
> Third, if things are done right, the bootstrap and the robust standard
> errors are asymptotically equivalent. Conceptually, you might be able
> to get some sort of second order improvements if you bootstrap the
> t-statistic and then refer the actual t-statistic value to your
> bootstrap distribution. But that's pretty convoluted, and it does not
> seem like you are interested in this.
>
> On Thu, Feb 16, 2012 at 10:14 PM, Danny Dan <danny2011dan@gmail.com> wrote:
>> Dear Friends,
>>
>> (1) I am trying to use both weights and vce(bootstrap) option in my
>> regression analysis as following:
>>
>> regress Y X (weight=wt), vce(bootstrap)
>>
>> The weights are generated using a Matching method, however, I cannot
>> do so as I am getting the following error:
>>
>> "Weights not allowed r(101);"
>>
>> I have tried using aweight, pweight, fweight and other weight options
>> available in STATA for regress and also sometimes getting the error
>> "may not use non-integer frequency weights r(401);".
>>
>> Therefore, nothing is working out. How can I use bootstrap option and
>> weight together in my regression?
>>
>> (2) Also is there anyway I can use both robust and bootstrap options
>> together with and without the weight option?
>
>
> --
> Stas Kolenikov, also found at http://stas.kolenikov.name
> Small print: I use this email account for mailing lists only.
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