Jannis:
1) the investgated -fe- is the one of the -panelid-;
2) -i.year- is a categorical predictor that account for within panel time-related variations. In the literature you can read taht this model is called two-way fixed effect, even though you cannit routunely retrieve the fixed efefct of time (wherea the community-contributed module -reghdfe- allows it);
3) more substantively, as you can see from the folowing toy-example, omitting -i.year- from the set of predictors reduces the within R-sq of the model (nad the same holds for -reghdfe-, too):
1) the investgated -fe- is the one of the -panelid-;
2) -i.year- is a categorical predictor that account for within panel time-related variations. In the literature you can read taht this model is called two-way fixed effect, even though you cannit routunely retrieve the fixed efefct of time (wherea the community-contributed module -reghdfe- allows it);
3) more substantively, as you can see from the folowing toy-example, omitting -i.year- from the set of predictors reduces the within R-sq of the model (nad the same holds for -reghdfe-, too):
Code:
. use "https://www.stata-press.com/data/r17/nlswork.dta"
(National Longitudinal Survey of Young Women, 14-24 years old in 1968)
. xtset idcode year
Panel variable: idcode (unbalanced)
Time variable: year, 68 to 88, but with gaps
Delta: 1 unit
. xtreg ln_wage i.year c.age##c.age, fe vce(cluster idcode)
Fixed-effects (within) regression Number of obs = 28,510
Group variable: idcode Number of groups = 4,710
R-squared: Obs per group:
Within = 0.1162 min = 1
Between = 0.1078 avg = 6.1
Overall = 0.0932 max = 15
F(16,4709) = 79.11
corr(u_i, Xb) = 0.0613 Prob > F = 0.0000
(Std. err. adjusted for 4,710 clusters in idcode)
------------------------------------------------------------------------------
| Robust
ln_wage | Coefficient std. err. t P>|t| [95% conf. interval]
-------------+----------------------------------------------------------------
year |
69 | .0647054 .0155249 4.17 0.000 .0342693 .0951415
70 | .0284423 .0264639 1.07 0.283 -.0234395 .080324
71 | .0579959 .0384111 1.51 0.131 -.0173078 .1332996
72 | .0510671 .0502675 1.02 0.310 -.0474808 .149615
73 | .0424104 .0624924 0.68 0.497 -.0801038 .1649247
75 | .0151376 .086228 0.18 0.861 -.1539096 .1841848
77 | .0340933 .1106841 0.31 0.758 -.1828994 .251086
78 | .0537334 .1232232 0.44 0.663 -.1878417 .2953084
80 | .0369475 .1473725 0.25 0.802 -.2519716 .3258667
82 | .0391687 .1715621 0.23 0.819 -.2971733 .3755108
83 | .058766 .1836086 0.32 0.749 -.3011928 .4187249
85 | .1042758 .2080199 0.50 0.616 -.3035406 .5120922
87 | .1242272 .2327328 0.53 0.594 -.3320379 .5804922
88 | .1904977 .2486083 0.77 0.444 -.2968909 .6778863
|
age | .0728746 .013687 5.32 0.000 .0460416 .0997075
|
c.age#c.age | -.0010113 .0001076 -9.40 0.000 -.0012224 -.0008003
|
_cons | .3937532 .2469015 1.59 0.111 -.0902893 .8777957
-------------+----------------------------------------------------------------
sigma_u | .40275174
sigma_e | .30127563
rho | .64120306 (fraction of variance due to u_i)
------------------------------------------------------------------------------
. xtreg ln_wage c.age##c.age, fe vce(cluster idcode)
Fixed-effects (within) regression Number of obs = 28,510
Group variable: idcode Number of groups = 4,710
R-squared: Obs per group:
Within = 0.1087 min = 1
Between = 0.1006 avg = 6.1
Overall = 0.0865 max = 15
F(2,4709) = 507.42
corr(u_i, Xb) = 0.0440 Prob > F = 0.0000
(Std. err. adjusted for 4,710 clusters in idcode)
------------------------------------------------------------------------------
| Robust
ln_wage | Coefficient std. err. t P>|t| [95% conf. interval]
-------------+----------------------------------------------------------------
age | .0539076 .004307 12.52 0.000 .0454638 .0623515
|
c.age#c.age | -.0005973 .000072 -8.30 0.000 -.0007384 -.0004562
|
_cons | .639913 .0624195 10.25 0.000 .5175415 .7622845
-------------+----------------------------------------------------------------
sigma_u | .4039153
sigma_e | .30245467
rho | .64073314 (fraction of variance due to u_i)
------------------------------------------------------------------------------
.
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