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| From | Fernando Rios Avila <f.rios.a@gmail.com> |
| 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]
--------------------+----------------------------------------------------------------
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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