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

[Danny Dan <danny2011dan@gmail.com>]

Thank you Stas. I will certainly look into those examples and if I can
find a suitable solution, I will post that on the Statalist so that
others facing similar problem may also be benefited.

Regards,

Dan

On Fri, Feb 17, 2012 at 8:17 AM, Stas Kolenikov <skolenik@gmail.com> wrote:
> Have you taken a look at the examples in the manual? They are kinda
> short, but they give you the ideas of how to approach the problem.
> There should also be plenty of similar small examples in statalist
> archives.
>
> On Fri, Feb 17, 2012 at 12:55 AM, Danny Dan <danny2011dan@gmail.com> wrote:
>> 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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>>
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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:
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> *   http://www.stata.com/support/statalist/faq
> *   http://www.ats.ucla.edu/stat/stata/

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