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

[Stas Kolenikov <skolenik@gmail.com>]

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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