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# Re: st: R2 and Xtreg vs areg

 From Fernando Rios Avila To statalist@hsphsun2.harvard.edu Subject Re: st: R2 and Xtreg vs areg Date Fri, 2 Mar 2012 13:28:58 -0500

```My apologies, I found the answer in the Stata FAQ. Thanks

On Fri, Mar 2, 2012 at 1:15 PM, Fernando Rios Avila <f.rios.a@gmail.com> wrote:
> 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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```