I have ran the following 2SLS fixed effect regression using --xtivreg-- however, the command with --fe-- do not produce ---adjusted r-squared--- as I have read through the documentation of the command. While looking into several other posts in the forum on the issue I have found some says to calculate manually while in another post shows that running the same regression with --regress-- might output equivalent adjusted r-sqaured from --xtreg-- (please note the forum member here talks about --xtreg-- command for re model --- this is just my interpretation for the sake of explaining my question)
Is there is any way I could derive the adjusted-r-squared in this case or I could also imply the r-adjusted squared from the regress command might be equal to the xtivreg--fe-- command ?
Thank you for your input.
Is there is any way I could derive the adjusted-r-squared in this case or I could also imply the r-adjusted squared from the regress command might be equal to the xtivreg--fe-- command ?
Thank you for your input.
Code:
. xtivreg ABS_DA_w POST_REG INBD POSTREG_X_REM ROA_w Size MTB_x_w LEV_w NOA_X_w (REM_PROXY_w = Lag_ABS_DA) i.
> Year, fe vce(robust) small
note: 2003.Year omitted because of collinearity.
Fixed-effects (within) IV regression Number of obs = 1,329
Group variable: id Number of groups = 283
R-squared: Obs per group:
Within = . min = 1
Between = 0.0027 avg = 4.7
Overall = 0.0001 max = 8
F( 298, 1031) = 0.09
corr(u_i, Xb) = -0.7857 Prob > F = 1.0000
(Std. err. adjusted for 283 clusters in id)
-------------------------------------------------------------------------------
| Robust
ABS_DA_w | Coefficient std. err. t P>|t| [95% conf. interval]
--------------+----------------------------------------------------------------
REM_PROXY_w | -8.190299 46.46889 -0.18 0.860 -99.3747 82.9941
POST_REG | .2102484 1.207148 0.17 0.862 -2.158498 2.578995
INBD | -.1738372 1.036343 -0.17 0.867 -2.20742 1.859746
POSTREG_X_REM | 5.371924 30.56678 0.18 0.861 -54.60828 65.35212
ROA_w | -1.486365 7.384874 -0.20 0.841 -15.97746 13.00473
Size | .2356881 1.24738 0.19 0.850 -2.212006 2.683382
MTB_x_w | -.1084283 .6490133 -0.17 0.867 -1.381966 1.165109
LEV_w | -.0466875 .4760218 -0.10 0.922 -.9807697 .8873947
NOA_X_w | .1026737 .5897351 0.17 0.862 -1.054544 1.259892
|
Year |
1997 | -.2185354 1.437328 -0.15 0.879 -3.038957 2.601886
1998 | -.0472755 .3712564 -0.13 0.899 -.7757799 .6812289
1999 | .0450469 .3317543 0.14 0.892 -.6059438 .6960377
2000 | -.0999352 .6051118 -0.17 0.869 -1.287327 1.087456
2001 | -.0248547 .237933 -0.10 0.917 -.4917429 .4420335
2002 | -.0231216 .3136842 -0.07 0.941 -.638654 .5924108
2003 | 0 (omitted)
|
_cons | -3.502738 18.88879 -0.19 0.853 -40.5676 33.56212
--------------+----------------------------------------------------------------
sigma_u | .92712421
sigma_e | .83874134
rho | .54992567 (fraction of variance due to u_i)
-------------------------------------------------------------------------------
Instrumented: REM_PROXY_w
Instruments: POST_REG INBD POSTREG_X_REM ROA_w Size MTB_x_w LEV_w NOA_X_w
1997.Year 1998.Year 1999.Year 2000.Year 2001.Year 2002.Year
Lag_ABS_DA
. ereturn list
scalars:
e(rank) = 15
e(df_rz) = 1031
e(N) = 1329
e(rss) = 725.2951365358066
e(N_g) = 283
e(N_clust) = 283
e(F) = .0924548469224322
e(F_p) = .9999974149191998
e(df_r) = 1031
e(sigma_u) = .9271242069970355
e(corr) = -.7856551784320752
e(r2_o) = .0001380057723449
e(r2_b) = .0027139040346453
e(sigma_e) = .838741341741927
e(sigma) = 1.250218514319369
e(rho) = .5499256655107171
e(r2_w) = .
e(df_m) = 298
e(df_a) = 282
e(F_fp) = 1
e(F_f) = .0134089563652
e(g_avg) = 4.696113074204947
e(g_max) = 8
e(g_min) = 1
macros:
e(cmdline) : "xtivreg ABS_DA_w POST_REG INBD POSTREG_X_REM ROA_w Size MTB_x_w LEV_w NOA_X_w (.."
e(cmd) : "xtivreg"
e(insts) : "POST_REG INBD POSTREG_X_REM ROA_w Size MTB_x_w LEV_w NOA_X_w 1996b.Year 1997.Ye.."
e(instd) : "REM_PROXY_w"
e(marginsok) : "XB default"
e(marginsnotok) : "ue xbu u e"
e(predict) : "xtivp_1"
e(depvar) : "ABS_DA_w"
e(model) : "fe"
e(tvar) : "Year"
e(ivar) : "id"
e(vce) : "robust"
e(clustvar) : "id"
e(vcetype) : "Robust"
e(small) : "small"
e(properties) : "b V"
matrices:
e(b) : 1 x 18
e(V) : 18 x 18
e(V_modelbased) : 18 x 18
functions:
e(sample)
