Garrett,
Kit answered your question, but I think it's also worth pointing out
that the standard errors probably can't be used either way. The
reason is that you have only 8 clusters. For the asymptotics of
cluster-robust covariance matrix estimation to work, the number of
cluster has to go off to infinity, and 8 is a long way from infinity.
It's like running a regression with 8 observations (and, in your
case, 7 unabosorbed regressors). If you click on the hyperlink of
the missing F-stat of your regression using -regress-, you'll get a
more detailed explanation.
--Mark
Date sent: Wed, 21 Jul 2004 17:43:14 -0700
From: Garrett Glasgow <[email protected]>
To: [email protected]
Subject: st: areg, regress, and cluster()
Send reply to: [email protected]
> Hi,
> I'm estimating a regression on how changing political party platforms affect
> vote shares. I included country-specific dummy variables, and I'm also using
> robust clustered standard errors (clustering on countries) as there's likely to
> be (negative) correlation between parties in vote share.
> I first estimated the model without clustering, first with areg, and then with
> regress and a set of dummy variables. As expected, the results were identical.
> However, when I add the cluster option it looks like Stata is making different
> corrections to the degrees of freedom in the t-test for statistical
> significance in these models, as well as doing some other things differently.
> The output from both models is below:
>
> . regress vgain vgainone ingovnow dirvshift pshift2a idparty idpshift
> Italy Britain Greece Luxembourg Denmark Netherlands Spain,
> cluster(ctrynum)
>
> Regression with robust standard errors Number of obs = 158
> F( 5, 7) = .
> Prob > F = .
> R-squared = 0.1477
> Number of clusters (ctrynum) = 8 Root MSE = 4.5572
>
> -----------------------------------------------------------------------
> | Robust
> vgain | Coef. Std. Err. t P>|t| [95% Conf. Interval]
> -------------+---------------------------------------------------------
> vgainone | -.1540838 .0951223 -1.62 0.149 -.3790123 .0708447
> ingovnow | -2.295443 .9807013 -2.34 0.052 -4.614434 .0235466
> dirvshift | 3.332989 1.629255 2.05 0.080 -.5195878 7.185565
> pshift2a | .808739 .8450504 0.96 0.370 -1.189488 2.806966
> idparty | .2054125 1.252439 0.16 0.874 -2.756136 3.166961
> idpshift | -3.21951 1.211969 -2.66 0.033 -6.085362 -.3536578
> _cons | -.1867649 .8090965 -0.23 0.824 -2.099974 1.726444
> (7 dummy varibles omitted)
> -----------------------------------------------------------------------
>
> . areg vgain vgainone ingovnow dirvshift pshift2a idparty idpshift,
> absorb(ctrynum) cluster(ctrynum)
>
> Regression with robust standard errors Number of obs = 158
> F( 5, 144) = 12.84
> Prob > F = 0.0000
> R-squared = 0.1477
> Adj R-squared = 0.0707
> Root MSE = 4.5572
> (standard errors adjusted for clustering on ctrynum)
> -----------------------------------------------------------------------
> | Robust
> vgain | Coef. Std. Err. t P>|t| [95% Conf. Interval]
> -------------+---------------------------------------------------------
> vgainone | -.1540838 .0951223 -1.62 0.107 -.3421002 .0339326
> ingovnow | -2.295443 .9807013 -2.34 0.021 -4.233873 -.3570138
> dirvshift | 3.332989 1.629255 2.05 0.043 .1126434 6.553334
> pshift2a | .808739 .8450504 0.96 0.340 -.8615666 2.479044
> idparty | .2054125 1.252439 0.16 0.870 -2.270128 2.680953
> idpshift | -3.21951 1.211969 -2.66 0.009 -5.615058 -.8239616
> _cons | .4581699 .8376469 0.55 0.585 -1.197502 2.113842
> -------------+----------------------------------------------------------
> ctrynum | absorbed
>
> As you can see, the coefficients, standard errors, and t-ratios are identical.
> However, the p-values associated with those t-ratios differs.
>
> What accounts for these differences?
>
> Thanks,
> Garrett
>
> *
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Prof. Mark E. Schaffer
Director
Centre for Economic Reform and Transformation
Department of Economics
School of Management & Languages
Heriot-Watt University, Edinburgh EH14 4AS UK
44-131-451-3494 direct
44-131-451-3008 fax
44-131-451-3485 CERT administrator
http://www.som.hw.ac.uk/cert
*
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