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# st: R2 and Xtreg vs areg

 From Fernando Rios Avila To statalist@hsphsun2.harvard.edu Subject st: R2 and Xtreg vs areg Date Fri, 2 Mar 2012 13:15:00 -0500

```Dear Statalisters,
I got an issue working with panel data fixed effects vs OLS including
dummies. Basically, Im trying to compare the goodness of fit of some
models, but i just realize that using xtreg vs areg give me different
R2s. Is there any reason explaining this kind of difference?
As an example compare this two models:
In the areg output we have an R2 of  0.69, in the xtreg model is only 0.26.

webuse nlswork
xtset idcode

xtreg ln_w grade age c.age#c.age ttl_exp c.ttl_exp#c.ttl_exp tenure
c.tenure#c.tenure 2.race not_smsa south, fe
note: grade omitted because of collinearity
note: 2.race omitted because of collinearity

Fixed-effects (within) regression               Number of obs      =     28091
Group variable: idcode                          Number of groups   =      4697

R-sq:  within  = 0.1727                         Obs per group: min =         1
between = 0.3505                                        avg =       6.0
overall = 0.2625                                        max =        15

F(8,23386)         =    610.12
corr(u_i, Xb)  = 0.1936                         Prob > F           =    0.0000

-------------------------------------------------------------------------------------
ln_wage |      Coef.   Std. Err.      t    P>|t|     [95%
Conf. Interval]
--------------------+----------------------------------------------------------------
age |   .0359987   .0033864    10.63   0.000
.0293611    .0426362
|
c.age#c.age |   -.000723   .0000533   -13.58   0.000
-.0008274   -.0006186
|
ttl_exp |   .0334668   .0029653    11.29   0.000
.0276545     .039279
|
c.ttl_exp#c.ttl_exp |   .0002163   .0001277     1.69   0.090
-.0000341    .0004666
|
tenure |   .0357539   .0018487    19.34   0.000
.0321303    .0393775
|
c.tenure#c.tenure |  -.0019701    .000125   -15.76   0.000
-.0022151   -.0017251
|
2.race |          0  (omitted)
not_smsa |  -.0890108   .0095316    -9.34   0.000
-.1076933   -.0703282
south |  -.0606309   .0109319    -5.55   0.000
-.0820582   -.0392036
_cons |    1.03732   .0485546    21.36   0.000
.9421496     1.13249
--------------------+----------------------------------------------------------------
sigma_u |  .35562203
sigma_e |  .29068923
rho |  .59946283   (fraction of variance due to u_i)
-------------------------------------------------------------------------------------
F test that all u_i=0:     F(4696, 23386) =     6.65         Prob > F = 0.0000

areg ln_w grade age c.age#c.age ttl_exp c.ttl_exp#c.ttl_exp tenure
c.tenure#c.tenure 2.race not_smsa south, absorb(idcode)
note: grade omitted because of collinearity
note: 2.race omitted because of collinearity

Linear regression, absorbing indicators           Number of obs   =      28091
F(   8,  23386) =     610.12
Prob > F        =     0.0000
R-squared       =     0.6919
Root MSE        =     0.2907

-------------------------------------------------------------------------------------
ln_wage |      Coef.   Std. Err.      t    P>|t|     [95%
Conf. Interval]
--------------------+----------------------------------------------------------------
age |   .0359987   .0033864    10.63   0.000
.0293611    .0426362
|
c.age#c.age |   -.000723   .0000533   -13.58   0.000
-.0008274   -.0006186
|
ttl_exp |   .0334668   .0029653    11.29   0.000
.0276545     .039279
|
c.ttl_exp#c.ttl_exp |   .0002163   .0001277     1.69   0.090
-.0000341    .0004666
|
tenure |   .0357539   .0018487    19.34   0.000
.0321303    .0393775
|
c.tenure#c.tenure |  -.0019701    .000125   -15.76   0.000
-.0022151   -.0017251
|
2.race |          0  (omitted)
not_smsa |  -.0890108   .0095316    -9.34   0.000
-.1076933   -.0703282
south |  -.0606309   .0109319    -5.55   0.000
-.0820582   -.0392036
_cons |    1.03732   .0485546    21.36   0.000
.9421496     1.13249
--------------------+----------------------------------------------------------------
idcode |    F(4696, 23386) =      6.653   0.000
(4697 categories)

thanks
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```