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Re: st: instrumental variable for quantile regression


From   "alessia matano" <alexis.rtd@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: instrumental variable for quantile regression
Date   Wed, 28 May 2008 18:35:36 +0200

Thank you Austin, for all your advices.
I will try it. I am not sure about the consistency of the estimates,
but as i understood, in applying such a procedure (whose i can also
use xtdata, fe) the problem is on the standard errors to be corrected
for the first stage. Anyway, i will try with some simulation.
many thanks for your patience


2008/5/28 Austin Nichols <austinnichols@gmail.com>:
> alessia matano <alexis.rtd@gmail.com>:
> I can't see where you got a repeated time values error.  As I said before:
> "show an example using a publicly available
> dataset, e.g. start your example code with
>  webuse psidextract, clear
>  tsset
> Probably you need at least the idcluster(newvar) option on
> -bootstrap-. "
>
> This estimation procedure you are using is wandering further and
> further afield; do you have any confidence it will generate consistent
> estimates of the parameter you are interested in?  As I understand it,
> you want to know the effect of some X on the conditional median of Y.
> If I were you, I would see how your cluster-bootstrapped time-demeaned
> qreg estimate does at recovering this parameter in some simulated
> data.
>
> On Wed, May 28, 2008 at 8:22 AM, alessia matano <alexis.rtd@gmail.com> wrote:
>> Dear Austin.
>>
>> You are right I tried to demean the data myself, since when I used
>> xtdata....., fe i(ind) it didn't create deviations from time average
>> for each individual (ind). But, then I asked myself if it does not in
>> fact do that, but just prepare the data for such a transoformation.
>> Any help regarding this is welcome.
>>
>> Secondly i applied a bootstrap procedure like the following (after
>> time demeaning the data)
>>
>> prog me
>>  qreg dmwage dmindchar dmspec  dmd1995-dmd2004 dmsectors dmarea, q(0.1)
>>  end
>> bs, cluster(ind): me
>>
>> I before tsset the data
>> tsset ind year
>> and it recognize that it is a panel. Then when I apply the program he
>> wrote repeated time values within panel. hence, my question about
>> clustering.
>> Moreover generally, in fixed effects estimates I also clustered the
>> observations for area (to deal with heteroskedasticity questions), and
>> I do not know if I can do it now.
>> Last, I tried the same with clustering() (see below) and it stopped at
>> the third bootstrap writing something like:
>> ..x
>>
>> prog me
>>  qreg dmwage dmindchar dmspec  dmd1995-dmd2004 dmsectors dmarea, q(0.1)
>>  end
>> bs, cluster(): me
>>
>> Thanks again for any help
>> alessia
>>
>> 2008/5/27 Austin Nichols <austinnichols@gmail.com>:
>>> alessia matano <alexis.rtd@gmail.com>:
>>> Perhaps if you show what you typed, folks on the list can offer
>>> advice.  Even better, show an example using a publicly available
>>> dataset, e.g. start your example code with
>>>
>>> webuse psidextract, clear
>>> tsset
>>>
>>> Probably you need at least the idcluster(newvar) option on
>>> -bootstrap-.  How are you demeaning yourself?  Better to use an -xt-
>>> commmand.
>>>
>>> On Tue, May 27, 2008 at 6:13 AM, alessia matano <alexis.rtd@gmail.com> wrote:
>>>> Dear Austin,
>>>>
>>>> I attempt to do the estimation with bootstrap....after time-demeaning
>>>> my observation. I have just a doubt. It is true that bootstrap allows
>>>> the cluster option, what i do not understand well is for what should i
>>>> clustered my observations to allow correct standard errors after
>>>> time-demenaing myself the observations. generally i clustered them not
>>>> for individual, but for provinces where they live (cause of
>>>> heteroskedasticity). However bootstrap either using the individual
>>>> cluster or using the province ones does not work, saying that there
>>>> are time repeated values within the panel, while when I tsset them it
>>>> understands the panel structure. May you help me with this also? It
>>>> works only when I cluster() with no variables inside.
>>>> May you suggest me something? Also to read about?
>>>> thank you very much
>>>> alessia
>>>>
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