----- Original Message -----
From: "tom blade" <[email protected]>
To: <[email protected]>
Sent: Wednesday, July 23, 2003 3:11 AM
Subject: st: robust standar errors in xtreg, re
> I would appreciate any help on the following topic.
>
> To get robust standar errors in a fixed effects panel data framework, I can
use:
> areg y x, absorb(iis) robust
>
> which gives the same coefficients as xtreg, fe but different standar errors.
>
> How could I obtain robust standar errors in the case of xtreg, re?
>
> Thank you very much.
>
> Tom
>
How about using -gllamm, roubst- its estimated coefficients seem to be very
close to those obtained by -xtreg, re-
Example:
. sysuse grunfeld
. gllamm invest mvalue kstock, robust i(com) adapt
Running adaptive quadrature
Iteration 1: log likelihood = -1131.7393
Iteration 2: log likelihood = -1111.848
Iteration 3: log likelihood = -1105.8103
Iteration 4: log likelihood = -1097.1931
Iteration 5: log likelihood = -1095.3823
Iteration 6: log likelihood = -1095.2697
Iteration 7: log likelihood = -1095.257
Iteration 8: log likelihood = -1095.257
Adaptive quadrature has converged, running Newton-Raphson
Iteration 0: log likelihood = -1095.257 (not concave)
Iteration 1: log likelihood = -1095.257
number of level 1 units = 200
number of level 2 units = 10
Condition Number = 3211.2392
gllamm model
log likelihood = -1095.257
Robust standard errors
------------------------------------------------------------------------------
invest | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
mvalue | .1097701 .0135518 8.10 0.000 .0832089 .1363312
kstock | .3079338 .0543002 5.67 0.000 .2015074 .4143602
_cons | -57.83158 24.36882 -2.37 0.018 -105.5936 -10.06957
------------------------------------------------------------------------------
Variance at level 1
------------------------------------------------------------------------------
2756.9637 (1142.8098)
Variances and covariances of random effects
------------------------------------------------------------------------------
***level 2 (com)
var(1): 6434.3487 (3504.6032)
------------------------------------------------------------------------------
. xtreg invest mvalue kstock, re
Random-effects GLS regression Number of obs = 200
Group variable (i): company Number of groups = 10
R-sq: within = 0.7668 Obs per group: min = 20
between = 0.8196 avg = 20.0
overall = 0.8061 max = 20
Random effects u_i ~ Gaussian Wald chi2(2) = 657.67
corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000
------------------------------------------------------------------------------
invest | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
mvalue | .1097811 .0104927 10.46 0.000 .0892159 .1303464
kstock | .308113 .0171805 17.93 0.000 .2744399 .3417861
_cons | -57.83441 28.89893 -2.00 0.045 -114.4753 -1.193537
-------------+----------------------------------------------------------------
sigma_u | 84.20095
sigma_e | 52.767964
rho | .71800838 (fraction of variance due to u_i)
------------------------------------------------------------------------------
.
Hope this helps,
Scott
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