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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 found by Nichols and Schaffer 2007 in unpublished work).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; >> >> * >> * For searches and help try: >> * http://www.stata.com/help.cgi?search >> * http://www.stata.com/support/statalist/faq >> * http://www.ats.ucla.edu/stat/stata/ >> > > > > -- > Thomas Jacobs > -- Thomas Jacobs * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**Re: st: Seemingly unrelated regression (SUR) test joint significance with clustered standard errors***From:*Thomas Jacobs <thomasjacobs@gmail.com>

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