[Gretl-users] It is necessary to use robust standard error in random effect estimation
When I run the code in the stata, the results are matching with gretl only when I specify
the robust standard errors. Why is this case?
xtreg ANS FDIENT FDICAP FDIAST FDITUR FDIGDP FDIROTC FDIROFA FDIROS FDIEMP FDICOM FDIWAG
GDPgrowth Size, re
Random-effects GLS regression Number of obs = 434
Group variable: Id Number of groups = 62
R-squared: Obs per group:
Within = 0.7754 min = 7
Between = 0.0372 avg = 7.0
Overall = 0.1280 max = 7
Wald chi2(13) = 525.11
corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000
------------------------------------------------------------------------------
ANS | Coefficient Std. err. z P>|z| [95% conf. interval]
-------------+----------------------------------------------------------------
FDIENT | .3538051 .1119847 3.16 0.002 .1343191 .5732911
FDICAP | .0560138 .0341982 1.64 0.101 -.0110135 .123041
FDIAST | -.0488365 .027242 -1.79 0.073 -.1022297 .0045567
FDITUR | -.0249275 .0195643 -1.27 0.203 -.0632728 .0134179
FDIGDP | .0761888 .0221277 3.44 0.001 .0328193 .1195583
FDIROTC | -.0140346 .0176176 -0.80 0.426 -.0485645 .0204953
FDIROFA | .0029775 .0053075 0.56 0.575 -.007425 .01338
FDIROS | -.0061958 .0053613 -1.16 0.248 -.0167037 .004312
FDIEMP | .0093037 .0294696 0.32 0.752 -.0484557 .0670631
FDICOM | -.0078266 .0252738 -0.31 0.757 -.0573623 .041709
FDIWAG | -.2533413 .3531677 -0.72 0.473 -.9455373 .4388548
GDPgrowth | .0565587 .0193329 2.93 0.003 .0186669 .0944505
Size | -12.14111 .6405544 -18.95 0.000 -13.39658 -10.88565
_cons | 69.71459 2.813264 24.78 0.000 64.2007 75.22849
-------------+----------------------------------------------------------------
sigma_u | 1.6051608
sigma_e | 1.0781735
rho | .68909913 (fraction of variance due to u_i)
------------------------------------------------------------------------------
Hausman test results are also different.
Hausman test -
Null hypothesis: GLS estimates are consistent
Asymptotic test statistic: Chi-square(13) = 341.943
with p-value = 3.83701e-65
Stata:
Test of H0: Difference in coefficients not systematic
chi2(13) = (b-B)'[(V_b-V_B)^(-1)](b-B)
= -918.16
Warning: chi2 < 0 ==> model fitted on these data
fails to meet the asymptotic assumptions
of the Hausman test; see suest for a
generalized test.
With sigmamore:
Test of H0: Difference in coefficients not systematic
chi2(13) = (b-B)'[(V_b-V_B)^(-1)](b-B)
= 237.83
Prob > chi2 = 0.0000
data:
https://www.dropbox.com/scl/fi/o9copqsun64if9n7vyu4r/2017035-data.dta?rlk...