Jessica <[email protected]>:
Thanks for your gracious reply.
The -cluster- option, not -robust-, is what makes your inference
robust to autocorrelation (both offer robustness to het). You might
try a standard fixed effects model, and compare SEs as follows:
g real=exp(lnreal1)
loc x "male yrsgrad yrsgradsq mar alumnicloserelative med_income
artsent bankfin commedia comptech consulting education environmental
govtpp healthcaremed intlang law nonprofitsocialservices
profservicesbus salesmarketing ownbusiness mileslt250 reunion soc
affinity acad devug arts campus sports otheract socsci natsci art
nongrad loans_z grants_z finaid_miss djia_pctch campaignyr hock_pc
rank hock_pc_male hock_pc_yrsgrad hock_pc_sports rank_male
rank_yrsgrad"
xtreg real `x' if stillug==0, fe i(pidm)
xtreg real `x' if stillug==0, fe i(pidm) robust
xtreg real `x' if stillug==0, fe i(pidm) cluster(pidm)
clogit donate `x' if stillug==0, group(pidm)
clogit donate `x' if stillug==0, group(pidm) robust
clogit donate `x' if stillug==0, group(pidm) cluster(pidm)
but see various programs from Baum and/or Schaffer (e.g. -xtivreg2-
and -ivreg2- and -xttest3- from SSC) for various tests for linear
regression and correct inference in the presence of AC without het.
The help file for -ivreg2- is a good starting point. A forthcoming
article in Stata Journal by Baum and Schaffer and Stillman provides
even more detail--watch for it...
On 7/9/07, Holmes, Jessica <[email protected]> wrote:
First, I want to thank everyone for their helpful responses to my query.
You all have been a fantastic resource!
I am still stuck on a test for autocorrelation, but this has been a
great help. If the -robust- standard errors correct for both
heteroscedasticity and autocorrelation and they are available in Stata
10, perhaps my problem is solved?
Thanks all!
Jessica
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