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From |
"Martin Weiss" <martin.weiss1@gmx.de> |

To |
<statalist@hsphsun2.harvard.edu> |

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

Date |
Thu, 5 Mar 2009 20:31:35 +0100 |

<> http://repec.org/usug2007/crse.pdf HTH Martin _______________________

To: <statalist@hsphsun2.harvard.edu> Sent: Thursday, March 05, 2009 8:27 PM

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. TomOn 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of a problem when testing a hypothesis that eats up more degrees of freedom (as found by Nichols and Schaffer 2007 in unpublished work).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.htmlRogers. 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 * * 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/

* * 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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