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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