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Re: st: Seemingly unrelated regression (SUR) test joint significance with clustered standard errors


From   Thomas Jacobs <thomasjacobs@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Seemingly unrelated regression (SUR) test joint significance with clustered standard errors
Date   Thu, 5 Mar 2009 13:29:48 -0600

Never mind, here is a working version for anyone interested,

http://repec.org/usug2007/crse.pdf

On Thu, Mar 5, 2009 at 1:27 PM, Thomas Jacobs <thomasjacobs@gmail.com> wrote:
> Austin,
>
> FYI, I have been unsuccessful in reading the slide presentation you
> linked from the Stata meeting.  On slide 10 I invariably get an I/O
> error and then the remaining slides are blank.  Is there an
> alternative source you can share a link to?  Thanks.
>
> Tom
>
> On Thu, Feb 26, 2009 at 9:32 AM, Austin Nichols <austinnichols@gmail.com> wrote:
>> Eric Lewis <erikylewis@gmail.com>:
>> I don't see an obvious bug in the code, but it is not likely that "the
>> -test- command is mis-programmed" as you surmise.  Much more likely
>> that the cluster-robust SE estimator you are using (note that -suest-
>> uses a form of cluster-robust SE estimation even in the absence of a
>> vce option) is biased downward, leading to over-rejection of the null
>> (a well-known if little appreciated feature of the cluster-robust SE
>> estimator; see Rogers 1993) .    This tends to be more of a problem
>> when testing a hypothesis that eats up more degrees of freedom (as
>> found by Nichols and Schaffer 2007 in unpublished work).
>>
>> In any case, you should compare your -suest- method to the standard
>> method of checking that treatment status is not correlated with
>> baseline characteristics--which is a comparison of means via
>> -hotelling- or an equivalent F-test in a regression of the treatment
>> indicator on baseline characteristics.  For example, suppose south is
>> the treatment indicator and you want to compare pre-treatment baseline
>> characteristics grade and wage:
>>
>> sysuse nlsw88, clear
>> hotelling grade wage, by(south)
>> qui reg south grade wage
>> di e(F)
>>
>> That model is a regression of treat on var* in your case:
>>
>> hotelling var*, by(treat)
>> reg treat var*
>>
>> which you can make cluster-robust:
>>
>> reg treat var*, vce(cluster clusterid)
>>
>> and to condition on level try something like:
>>
>> loc xi
>> unab v: var*
>> foreach i of local v {
>>  if "`xi'"=="" loc xi "`xi' i.level*`i'"
>>  else loc xi "`xi' i.level|`i'"
>> }
>> xi: reg treat `xi', vce(cluster clusterid)
>>
>> and let us know what the result is...
>>
>> Nichols and Schaffer. 2007. http://www.stata.com/meeting/13uk/abstracts.html
>> Rogers. 1993. http://www.stata.com/support/faqs/stat/stb13_rogers.pdf
>>
>>
>> On Wed, Feb 25, 2009 at 6:28 PM, Eric Lewis <erikylewis@gmail.com> wrote:
>>> Hi,
>>>
>>> I was working on some analysis of an experiment where I am checking to
>>> make sure that treatment status is not correlated to baseline
>>> characteristics conditional on an exogenous category ("level") where
>>> standard errors are clustered.  To get one single statistic, I was
>>> combining variables using a SUR model.  I kept getting a rejection of
>>> null hypothesis, and wondered if the test program is not written
>>> correctly.  So I wrote a monte carlo simulation to check the quality
>>> of the "test" command and it looks like indeed the "test" command is
>>> mis-programmed.  The simulation code is posted below, and you can
>>> check out the high fraction of p values that reject.  Simulations
>>> without the cluster command seem to give a much more reasonable
>>> distribution of p values.
>>>
>>> Does anyone know of some alternative way of testing joint significance
>>> in SUR with clustered standard errors?
>>> (Or perhaps there's a bug in my simulation code . . .)
>>>
>>> Thanks,
>>>
>>> Eric
>>>
>>> #delimit ;
>>> cap program drop jointtest ;
>>> program define jointtest, rclass ;
>>>        #delimit ;
>>>        est clear ;
>>>        drop _all ;
>>>        set obs 6000 ;
>>>        gen clusterid = ceil(_n*280/_N) ;
>>>        forvalues i = 1(1)19 { ;
>>>                gen var`i' = invnormal(uniform()) ;
>>>        } ;
>>>        egen level = fill(1 2 3 4 1 2 3 4) ;
>>>        gen uniform = uniform() ;
>>>        gen treat = (uniform > .5);
>>>        gen treat2 = (uniform >= .25 & uniform < .5) ;
>>>        gen treat3 = (uniform >= .50 & uniform < .75) ;
>>>        gen treat4 = (uniform >= .75) ;
>>>
>>>        foreach level in 1 2 3 4 { ;
>>>                foreach var of varlist var* { ;
>>>                        regress `var' treat if level == `level' ;
>>>                        * regress `var' treat2 treat3 treat4 if level == `level' ;
>>>                        est store e_`var'_`level' ;
>>>                } ;
>>>        } ;
>>>
>>>        suest e_*_* , vce(cluster clusterid);
>>>        testparm treat;
>>>        * testparm treat2 treat3 treat4;
>>>                return scalar chi = r(chi2);
>>>                return scalar df = r(df);
>>>                return scalar p = r(p);
>>> end;
>>>
>>> simulate chitest = r(chi) dftest = r(df) ptest = r(p), reps(100): jointtest;
>>> tab ptest;
>>> gen frac05 = (ptest < .05);
>>> tab frac05;
>>
>> *
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>>
>
>
>
> --
> Thomas Jacobs
>



-- 
Thomas Jacobs

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