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st:Re:Re: robust standar errors in xtreg, re


From   "tom blade" <tomblade@eudoramail.com>
To   statalist@hsphsun2.harvard.edu
Subject   st:Re:Re: robust standar errors in xtreg, re
Date   Thu, 24 Jul 2003 13:35:58 -0400

Thanks Scott!

> 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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