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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