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st: A question about fixed-effect regression


From   "Hui Wang" <jackie_stata@hotmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   st: A question about fixed-effect regression
Date   Fri, 03 Nov 2006 20:08:12 +0000

Hi, All,

Here is a question about the fixed-effect regression. The following is a dataset:

id y x1 x2
--------------------------
1 4 1 2
1 13 0 6
1 2 0 5
--------------------------
2 5 0 7
2 7 0 9
--------------------------
3 9 1 5
3 10 1 3
3 12 0 1
--------------------------
4 12 1 4
4 15 1 7
--------------------------

I want to estimate the effect of x1 on y (controlling for x2) using fixed effect regression. The command is

.iis id
.xtreg y x1 x2, fe

which generates the results:
------------------------------------------------------------------------------
y | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
x1 | -2.857401 3.369186 -0.85 0.444 -12.21176 6.496959
x2 | .5703971 .8082814 0.71 0.519 -1.673752 2.814546
_cons | 7.533755 4.564256 1.65 0.174 -5.138651 20.20616
-------------+----------------------------------------------------------------


But notice that for id=2 and id=4, x1 does not vary within the same id. If the fixed effect realy make use of the

variation within the same id, may be I should exclude these observations. The command is:

.xtreg y x1 x2 if id~=2 & id~=4, fe

which generates the results:
------------------------------------------------------------------------------
y | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
x1 | -2.899497 4.701649 -0.62 0.600 -23.12906 17.33006
x2 | .4020101 1.329827 0.30 0.791 -5.319774 6.123794
_cons | 8.309045 5.984222 1.39 0.299 -17.43898 34.05707
-------------+----------------------------------------------------------------

The coefficient on x1 in these two regressions are closed but not exactly the same, so are their standard errors.

My question is: which one make more sense?

Thank you.

Jackie

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