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 <[email protected]> wrote:
> Eric Lewis <[email protected]>:
> 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 found by Nichols and Schaffer 2007 in unpublished work).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 <[email protected]> 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
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