Stata users:
I'am using the "xtreg" command to run random effects models on panel data (unbalanced panel).
My unit of analysis is Korea government agencies.
My question concerns the "r-squared between" and "r-squared overall".
In model 1, there are no quadratic terms. In model 2, I include 4 quadratic terms.
Though one of these quadratic term is statistically significant, "r-squared between" and "r-squared overall" declines.
As far as I know, r-squared (not adjusted r-squared) is supposed to never decrease when variables are added to a model.
Does this apply differently to panel r-squared?
I know that xtreg, fe calculates r-squared differently from areg. But my model is RE.
I would appreciate any help regarding this issue.
# delimit
xtreg reput c.c_z_coder12_r c.c_z_sv_amb_di_r c.c_z_amb_ev c.c_z_amb_pri
c_sq_age2 c_ln_fi_total c_ln_size_full c_sq_up5_r c_fi_ex_ratio c_factor_cen c_factor_pbase
i.org_type i.year
, re vce(cl org) theta;
# delimit cr
# delimit
xtreg reput c.c_z_coder12_r##c.c_z_coder12_r c.c_z_sv_amb_di_r##c.c_z_sv_amb_di_r c.c_z_amb_ev##c.c_z_amb_ev c.c_z_amb_pri##c.c_z_amb_pri
c_sq_age2 c_ln_fi_total c_ln_size_full c_sq_up5_r c_fi_ex_ratio c_factor_cen c_factor_pbase
i.org_type i.year
, re vce(cl org) theta;
# delimit cr
Random-effects GLS regression Number of obs = 228
Group variable: org Number of groups = 44
R-sq: Obs per group:
within = 0.0994 min = 1
between = 0.3990 avg = 5.2
overall = 0.3377 max = 7
Wald chi2(19) = 70.29
corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000
------------------- theta --------------------
min 5% median 95% max
0.3668 0.4993 0.7046 0.7046 0.7046
(Std. Err. adjusted for 44 clusters in org)
---------------------------------------------------------------------------------
| Robust
reput | Coef. Std. Err. z P>|z| [95% Conf. Interval]
----------------+----------------------------------------------------------------
c_z_coder12_r | -.1582868 .121043 -1.31 0.191 -.3955268 .0789532
c_z_sv_amb_di_r | .1300482 .0996248 1.31 0.192 -.0652129 .3253093
c_z_amb_ev | .2304708 .0934306 2.47 0.014 .0473501 .4135914
c_z_amb_pri | .1374347 .1141718 1.20 0.229 -.0863379 .3612073
c_sq_age2 | .0527441 .0909437 0.58 0.562 -.1255023 .2309906
c_ln_fi_total | -.1303679 .0615055 -2.12 0.034 -.2509165 -.0098193
c_ln_size_full | -.5278446 .2200759 -2.40 0.016 -.9591855 -.0965037
c_sq_up5_r | -.7677916 1.11694 -0.69 0.492 -2.956955 1.421371
c_fi_ex_ratio | 3.959733 5.413107 0.73 0.464 -6.649762 14.56923
c_factor_pbase | .2466075 .1336372 1.85 0.065 -.0153165 .5085316
c_factor_cen | -.1580916 .1283419 -1.23 0.218 -.4096371 .0934538
|
org_type |
2 | .3448319 .5811616 0.59 0.553 -.794224 1.483888
3 | -.0150424 .4863613 -0.03 0.975 -.968293 .9382083
|
year |
2012 | .5635269 .2108262 2.67 0.008 .1503152 .9767386
2013 | .5536817 .260991 2.12 0.034 .0421486 1.065215
2014 | .6628218 .3493855 1.90 0.058 -.0219612 1.347605
2015 | .7331962 .3281427 2.23 0.025 .0900483 1.376344
2016 | .4634112 .3310233 1.40 0.162 -.1853825 1.112205
2017 | .753503 .2953417 2.55 0.011 .1746438 1.332362
|
_cons | -.4609324 .4146254 -1.11 0.266 -1.273583 .3517184
----------------+----------------------------------------------------------------
sigma_u | 1.0696963
sigma_e | .8750723
rho | .59908334 (fraction of variance due to u_i)
---------------------------------------------------------------------------------
Random-effects GLS regression Number of obs = 228
Group variable: org Number of groups = 44
R-sq: Obs per group:
within = 0.1197 min = 1
between = 0.3884 avg = 5.2
overall = 0.3240 max = 7
Wald chi2(23) = 78.44
corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000
------------------- theta --------------------
min 5% median 95% max
0.3962 0.5278 0.7247 0.7247 0.7247
(Std. Err. adjusted for 44 clusters in org)
---------------------------------------------------------------------------------------------
| Robust
reput | Coef. Std. Err. z P>|z| [95% Conf. Interval]
----------------------------+----------------------------------------------------------------
c_z_coder12_r | -.3011932 .1001225 -3.01 0.003 -.4974297 -.1049568
|
c.c_z_coder12_r#|
c.c_z_coder12_r | .1389741 .0609769 2.28 0.023 .0194616 .2584866
|
c_z_sv_amb_di_r | .1418791 .1084855 1.31 0.191 -.0707486 .3545068
|
c.c_z_sv_amb_di_r#|
c.c_z_sv_amb_di_r | -.0106288 .0501871 -0.21 0.832 -.1089937 .0877362
|
c_z_amb_ev | .2514924 .1086162 2.32 0.021 .0386086 .4643762
|
c.c_z_amb_ev#c.c_z_amb_ev | -.0153864 .0526041 -0.29 0.770 -.1184885 .0877157
|
c_z_amb_pri | .1215835 .1303769 0.93 0.351 -.1339506 .3771175
|
c.c_z_amb_pri#c.c_z_amb_pri | .0300072 .0432316 0.69 0.488 -.0547252 .1147395
|
c_sq_age2 | .0359767 .0931003 0.39 0.699 -.1464964 .2184499
c_ln_fi_total | -.1343288 .0637824 -2.11 0.035 -.2593401 -.0093176
c_ln_size_full | -.5118246 .2192068 -2.33 0.020 -.9414621 -.0821871
c_sq_up5_r | -.4459308 1.165331 -0.38 0.702 -2.729937 1.838075
c_fi_ex_ratio | 4.136125 5.672302 0.73 0.466 -6.981382 15.25363
c_factor_cen | -.1714061 .1405665 -1.22 0.223 -.4469115 .1040992
c_factor_pbase | .2743009 .1450345 1.89 0.059 -.0099614 .5585632
|
org_type |
2 | .376366 .5685421 0.66 0.508 -.7379561 1.490688
3 | .1222509 .4832368 0.25 0.800 -.8248757 1.069378
|
year |
2012 | .5509363 .2144268 2.57 0.010 .1306675 .9712051
2013 | .6018072 .2458745 2.45 0.014 .119902 1.083712
2014 | .7005422 .3292841 2.13 0.033 .0551572 1.345927
2015 | .7299815 .2984586 2.45 0.014 .1450134 1.31495
2016 | .4727062 .3103182 1.52 0.128 -.1355062 1.080919
2017 | .8041507 .2874125 2.80 0.005 .2408324 1.367469
|
_cons | -.6848513 .4418399 -1.55 0.121 -1.550842 .181139
----------------------------+----------------------------------------------------------------
sigma_u | 1.1598991
sigma_e | .87865805
rho | .63538406 (fraction of variance due to u_i)
---------------------------------------------------------------------------------------------
I'am using the "xtreg" command to run random effects models on panel data (unbalanced panel).
My unit of analysis is Korea government agencies.
My question concerns the "r-squared between" and "r-squared overall".
In model 1, there are no quadratic terms. In model 2, I include 4 quadratic terms.
Though one of these quadratic term is statistically significant, "r-squared between" and "r-squared overall" declines.
As far as I know, r-squared (not adjusted r-squared) is supposed to never decrease when variables are added to a model.
Does this apply differently to panel r-squared?
I know that xtreg, fe calculates r-squared differently from areg. But my model is RE.
I would appreciate any help regarding this issue.
# delimit
xtreg reput c.c_z_coder12_r c.c_z_sv_amb_di_r c.c_z_amb_ev c.c_z_amb_pri
c_sq_age2 c_ln_fi_total c_ln_size_full c_sq_up5_r c_fi_ex_ratio c_factor_cen c_factor_pbase
i.org_type i.year
, re vce(cl org) theta;
# delimit cr
# delimit
xtreg reput c.c_z_coder12_r##c.c_z_coder12_r c.c_z_sv_amb_di_r##c.c_z_sv_amb_di_r c.c_z_amb_ev##c.c_z_amb_ev c.c_z_amb_pri##c.c_z_amb_pri
c_sq_age2 c_ln_fi_total c_ln_size_full c_sq_up5_r c_fi_ex_ratio c_factor_cen c_factor_pbase
i.org_type i.year
, re vce(cl org) theta;
# delimit cr
Random-effects GLS regression Number of obs = 228
Group variable: org Number of groups = 44
R-sq: Obs per group:
within = 0.0994 min = 1
between = 0.3990 avg = 5.2
overall = 0.3377 max = 7
Wald chi2(19) = 70.29
corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000
------------------- theta --------------------
min 5% median 95% max
0.3668 0.4993 0.7046 0.7046 0.7046
(Std. Err. adjusted for 44 clusters in org)
---------------------------------------------------------------------------------
| Robust
reput | Coef. Std. Err. z P>|z| [95% Conf. Interval]
----------------+----------------------------------------------------------------
c_z_coder12_r | -.1582868 .121043 -1.31 0.191 -.3955268 .0789532
c_z_sv_amb_di_r | .1300482 .0996248 1.31 0.192 -.0652129 .3253093
c_z_amb_ev | .2304708 .0934306 2.47 0.014 .0473501 .4135914
c_z_amb_pri | .1374347 .1141718 1.20 0.229 -.0863379 .3612073
c_sq_age2 | .0527441 .0909437 0.58 0.562 -.1255023 .2309906
c_ln_fi_total | -.1303679 .0615055 -2.12 0.034 -.2509165 -.0098193
c_ln_size_full | -.5278446 .2200759 -2.40 0.016 -.9591855 -.0965037
c_sq_up5_r | -.7677916 1.11694 -0.69 0.492 -2.956955 1.421371
c_fi_ex_ratio | 3.959733 5.413107 0.73 0.464 -6.649762 14.56923
c_factor_pbase | .2466075 .1336372 1.85 0.065 -.0153165 .5085316
c_factor_cen | -.1580916 .1283419 -1.23 0.218 -.4096371 .0934538
|
org_type |
2 | .3448319 .5811616 0.59 0.553 -.794224 1.483888
3 | -.0150424 .4863613 -0.03 0.975 -.968293 .9382083
|
year |
2012 | .5635269 .2108262 2.67 0.008 .1503152 .9767386
2013 | .5536817 .260991 2.12 0.034 .0421486 1.065215
2014 | .6628218 .3493855 1.90 0.058 -.0219612 1.347605
2015 | .7331962 .3281427 2.23 0.025 .0900483 1.376344
2016 | .4634112 .3310233 1.40 0.162 -.1853825 1.112205
2017 | .753503 .2953417 2.55 0.011 .1746438 1.332362
|
_cons | -.4609324 .4146254 -1.11 0.266 -1.273583 .3517184
----------------+----------------------------------------------------------------
sigma_u | 1.0696963
sigma_e | .8750723
rho | .59908334 (fraction of variance due to u_i)
---------------------------------------------------------------------------------
Random-effects GLS regression Number of obs = 228
Group variable: org Number of groups = 44
R-sq: Obs per group:
within = 0.1197 min = 1
between = 0.3884 avg = 5.2
overall = 0.3240 max = 7
Wald chi2(23) = 78.44
corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000
------------------- theta --------------------
min 5% median 95% max
0.3962 0.5278 0.7247 0.7247 0.7247
(Std. Err. adjusted for 44 clusters in org)
---------------------------------------------------------------------------------------------
| Robust
reput | Coef. Std. Err. z P>|z| [95% Conf. Interval]
----------------------------+----------------------------------------------------------------
c_z_coder12_r | -.3011932 .1001225 -3.01 0.003 -.4974297 -.1049568
|
c.c_z_coder12_r#|
c.c_z_coder12_r | .1389741 .0609769 2.28 0.023 .0194616 .2584866
|
c_z_sv_amb_di_r | .1418791 .1084855 1.31 0.191 -.0707486 .3545068
|
c.c_z_sv_amb_di_r#|
c.c_z_sv_amb_di_r | -.0106288 .0501871 -0.21 0.832 -.1089937 .0877362
|
c_z_amb_ev | .2514924 .1086162 2.32 0.021 .0386086 .4643762
|
c.c_z_amb_ev#c.c_z_amb_ev | -.0153864 .0526041 -0.29 0.770 -.1184885 .0877157
|
c_z_amb_pri | .1215835 .1303769 0.93 0.351 -.1339506 .3771175
|
c.c_z_amb_pri#c.c_z_amb_pri | .0300072 .0432316 0.69 0.488 -.0547252 .1147395
|
c_sq_age2 | .0359767 .0931003 0.39 0.699 -.1464964 .2184499
c_ln_fi_total | -.1343288 .0637824 -2.11 0.035 -.2593401 -.0093176
c_ln_size_full | -.5118246 .2192068 -2.33 0.020 -.9414621 -.0821871
c_sq_up5_r | -.4459308 1.165331 -0.38 0.702 -2.729937 1.838075
c_fi_ex_ratio | 4.136125 5.672302 0.73 0.466 -6.981382 15.25363
c_factor_cen | -.1714061 .1405665 -1.22 0.223 -.4469115 .1040992
c_factor_pbase | .2743009 .1450345 1.89 0.059 -.0099614 .5585632
|
org_type |
2 | .376366 .5685421 0.66 0.508 -.7379561 1.490688
3 | .1222509 .4832368 0.25 0.800 -.8248757 1.069378
|
year |
2012 | .5509363 .2144268 2.57 0.010 .1306675 .9712051
2013 | .6018072 .2458745 2.45 0.014 .119902 1.083712
2014 | .7005422 .3292841 2.13 0.033 .0551572 1.345927
2015 | .7299815 .2984586 2.45 0.014 .1450134 1.31495
2016 | .4727062 .3103182 1.52 0.128 -.1355062 1.080919
2017 | .8041507 .2874125 2.80 0.005 .2408324 1.367469
|
_cons | -.6848513 .4418399 -1.55 0.121 -1.550842 .181139
----------------------------+----------------------------------------------------------------
sigma_u | 1.1598991
sigma_e | .87865805
rho | .63538406 (fraction of variance due to u_i)
---------------------------------------------------------------------------------------------
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