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Re: st: R2 and Xtreg vs areg
From
Fernando Rios Avila <[email protected]>
To
[email protected]
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 <[email protected]> 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]
> --------------------+----------------------------------------------------------------
> grade | 0 (omitted)
> 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
> Adj R-squared = 0.6299
> Root MSE = 0.2907
>
> -------------------------------------------------------------------------------------
> ln_wage | Coef. Std. Err. t P>|t| [95%
> Conf. Interval]
> --------------------+----------------------------------------------------------------
> grade | 0 (omitted)
> 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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