Dear Kit
Thank you. As I mentioned I used -xtreg- for the
original equation as I believed that random-effects
GLS is a less biased estimator than OLS, since the
data are grouped across workplaces (Moulton, 1987). A
Hausman test easily rejects the fixed effects GLS.
I tried to perform the Hausman test by estimating the
model with -xtivreg- using re. But this is what I got:
. xtivreg pay yrsed (b4a2 = dhat) mnedwk2 e1a1
p1a2-p1a5 v2a1-v2a5 v14a1 dis2 h2a2 h2a3 h4a2 h3a1
h3a3 h3a4 f1a1 z2-z6 ratiopk2 n1-n4 n6-n12
fnonmana1-fn6 fno1-fno6 ratiofk2 aga1 k2-k4 in1 c1,
re
no room to add more variables
An attempt was made to add a variable that would
have resulted in more than 2048 or 2047 variables
(Stata reserves one variable for its own use).
You have the following alternatives:
1. Drop some variables; see help drop.
2. If you are using Stata/SE, increase maxvar;
see help maxvar.
r(900);
As I am suing Intercooled Stata; option is not
applicable to me.
I would appreciate your comments immensely.
Thank you
Kit Baum wrote:
I guess I don't understand your setup. You're using
xtreg, which is appropriate for panel data. If the
original regression is
.xtreg y training meanworkplacetraining x2 x3 x4
and there is a possibility that training is
endogenous, you definitely are violating the key
assumption of the random-effects model, that the
regressors are distributed independently of the
errors. You should be estimating this equation
allowing for endogeneity of the suspected variable,
and need to perform a Hausman test of the
appropriateness of the key assumption. You can do the
Hausman test by estimating the model with -xtivreg-
using RE and FE. If you find that the RE assumption
cannot be maintained, then use - xtivreg2- with fixed
effects and use the -endog()- option to test whether
the endogeneity is an issue.
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