Omitted values of country fixed effect in Fixed Effects Model

[Sulaman Muhammad]

I use country fixed effect as i.id. Id is the countries list in panel, total 27 countries. However, the dummy variables (i.id) values omit. what's the problem ??


xtreg lnepi diver depen gpor con i.id, fe
note: 2.id omitted because of collinearity
note: 3.id omitted because of collinearity
note: 4.id omitted because of collinearity
note: 5.id omitted because of collinearity
note: 6.id omitted because of collinearity
note: 7.id omitted because of collinearity
note: 8.id omitted because of collinearity
note: 9.id omitted because of collinearity
note: 10.id omitted because of collinearity
note: 11.id omitted because of collinearity
note: 12.id omitted because of collinearity
note: 13.id omitted because of collinearity
note: 14.id omitted because of collinearity
note: 15.id omitted because of collinearity
note: 16.id omitted because of collinearity
note: 17.id omitted because of collinearity
note: 18.id omitted because of collinearity
note: 19.id omitted because of collinearity
note: 20.id omitted because of collinearity
note: 21.id omitted because of collinearity
note: 22.id omitted because of collinearity
note: 23.id omitted because of collinearity
note: 24.id omitted because of collinearity
note: 25.id omitted because of collinearity
note: 26.id omitted because of collinearity
note: 27.id omitted because of collinearity

Fixed-effects (within) regression Number of obs = 478
Group variable: id Number of groups = 27

R-sq: Obs per group:
within = 0.2128 min = 10
between = 0.1196 avg = 17.7
overall = 0.0373 max = 18

F(4,447) = 30.21
corr(u_i, Xb) = -0.9355 Prob > F = 0.0000


lnepi Coef. Std. Err. t P>t [95% Conf. Interval]

diver -.3900235 .0712686 -5.47 0.000 -.5300867 -.2499603
depen -.0869759 .0194187 -4.48 0.000 -.1251392 -.0488126
gpor .0762414 .0446314 1.71 0.088 -.0114721 .1639548
con -.1036351 .030633 -3.38 0.001 -.1638378 -.0434325

id
2 0 (omitted)
3 0 (omitted)
4 0 (omitted)
5 0 (omitted)
6 0 (omitted)
7 0 (omitted)
8 0 (omitted)
9 0 (omitted)
10 0 (omitted)
11 0 (omitted)
12 0 (omitted)
13 0 (omitted)
14 0 (omitted)
15 0 (omitted)
16 0 (omitted)
17 0 (omitted)
18 0 (omitted)
19 0 (omitted)
20 0 (omitted)
21 0 (omitted)
22 0 (omitted)
23 0 (omitted)
24 0 (omitted)
25 0 (omitted)
26 0 (omitted)
27 0 (omitted)

_cons 4.261741 .1236697 34.46 0.000 4.018695 4.504787

sigma_u .13627618
sigma_e .09850066
rho .65683932 (fraction of variance due to u_i)

F test that all u_i=0: F(26, 447) = 4.06 Prob > F = 0.0000

[Carlo Lazzaro]

Sulaman:
welcome to this forum.
This is more than expected: your -i.id- predictor is perfectly collinear with your -id- fixed effect !
You can torture linear algebra as you like, but data will not tell you what your ask them to spit out.
Try:

regress lnepi diver depen gpor con i.id

and compare it to:

xtset id year
xtreg lnepi diver depen gpor con, fe

The following toy-example might be useful:

. use "https://www.stata-press.com/data/r18/nlswork.dta"
(National Longitudinal Survey of Young Women, 14-24 years old in 1968)
. xtset idcode year
. xtreg ln_wage i.idcode i.year if idcode<=3, fe
note: 2.idcode omitted because of collinearity.
note: 3.idcode omitted because of collinearity.

Fixed-effects (within) regression               Number of obs     =         39
Group variable: idcode                          Number of groups  =          3

R-squared:                                      Obs per group:
     Within  = 0.5446                                         min =         12
     Between = 0.2670                                         avg =       13.0
     Overall = 0.3678                                         max =         15

                                                F(14, 22)         =       1.88
corr(u_i, Xb) = -0.0356                         Prob > F          =     0.0897

------------------------------------------------------------------------------
     ln_wage | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      idcode |
          2  |          0  (omitted)
          3  |          0  (omitted)
             |
        year |
         69  |    .208967   .3918928     0.53   0.599    -.6037689    1.021703
         70  |  -.2747772   .3439816    -0.80   0.433    -.9881514    .4385969
         71  |  -.3613911    .326316    -1.11   0.280    -1.038129    .3153467
         72  |  -.2056973    .326316    -0.63   0.535    -.8824352    .4710406
         73  |  -.0310461    .326316    -0.10   0.925     -.707784    .6456917
         75  |   .0416271    .326316     0.13   0.900    -.6351107     .718365
         77  |   .0358937    .326316     0.11   0.913    -.6408441    .7126316
         78  |   .2433199    .326316     0.75   0.464    -.4334179    .9200578
         80  |   .2726139    .326316     0.84   0.412    -.4041239    .9493518
         82  |   .1747839   .3439816     0.51   0.616    -.5385903    .8881581
         83  |   .2924489    .326316     0.90   0.380    -.3842889    .9691868
         85  |   .3712589    .326316     1.14   0.267     -.305479    1.047997
         87  |   .2960361    .326316     0.91   0.374    -.3807017     .972774
         88  |   .3038639    .326316     0.93   0.362    -.3728739    .9806018
             |
       _cons |   1.659677   .2833366     5.86   0.000     1.072073    2.247281
-------------+----------------------------------------------------------------
     sigma_u |  .24956596
     sigma_e |  .27711004
         rho |  .44784468   (fraction of variance due to u_i)
------------------------------------------------------------------------------
F test that all u_i=0: F(2, 22) = 9.64                       Prob > F = 0.0010


. regress ln_wage i.idcode i.year if idcode<=3

      Source |       SS           df       MS      Number of obs   =        39
-------------+----------------------------------   F(16, 22)       =      2.84
       Model |  3.48635949        16  .217897468   Prob > F        =    0.0122
    Residual |  1.68937946        22  .076789976   R-squared       =    0.6736
-------------+----------------------------------   Adj R-squared   =    0.4362
       Total |  5.17573896        38  .136203657   Root MSE        =    .27711

------------------------------------------------------------------------------
     ln_wage | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
      idcode |
          2  |  -.3898423   .1155629    -3.37   0.003     -.629505   -.1501795
          3  |  -.4648596   .1120424    -4.15   0.000    -.6972212   -.2324979
             |
        year |
         69  |    .208967   .3918928     0.53   0.599    -.6037689    1.021703
         70  |  -.2747772   .3439816    -0.80   0.433    -.9881514    .4385969
         71  |  -.3613911    .326316    -1.11   0.280    -1.038129    .3153467
         72  |  -.2056973    .326316    -0.63   0.535    -.8824352    .4710406
         73  |  -.0310461    .326316    -0.10   0.925     -.707784    .6456917
         75  |   .0416271    .326316     0.13   0.900    -.6351107     .718365
         77  |   .0358937    .326316     0.11   0.913    -.6408441    .7126316
         78  |   .2433199    .326316     0.75   0.464    -.4334179    .9200578
         80  |   .2726139    .326316     0.84   0.412    -.4041239    .9493518
         82  |   .1747839   .3439816     0.51   0.616    -.5385903    .8881581
         83  |   .2924489    .326316     0.90   0.380    -.3842889    .9691868
         85  |   .3712589    .326316     1.14   0.267     -.305479    1.047997
         87  |   .2960361    .326316     0.91   0.374    -.3807017     .972774
         88  |   .3038639    .326316     0.93   0.362    -.3728739    .9806018
             |
       _cons |   1.958421   .2989038     6.55   0.000     1.338532    2.578309
------------------------------------------------------------------------------

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