panel data regression multicollinearity problem

[Adam Klaas]

How can i solve multicolinearity problem?

This is my regression from stata:

> e)
note: 2018.Year omitted because of collinearity

Fixed-effects (within) regression               Number of obs     =      2,256
Group variable: GM_code                         Number of groups  =        282

R-sq:                                           Obs per group:
     within  = 0.9240                                         min =          8
     between = 0.4510                                         avg =        8.0
     overall = 0.2389                                         max =          8

                                                F(11,281)         =    1051.65
corr(u_i, Xb)  = 0.1606                         Prob > F          =     0.0000

                                       (Std. Err. adjusted for 282 clusters in GM_code)
---------------------------------------------------------------------------------------
                      |               Robust
           log_realHP |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
----------------------+----------------------------------------------------------------
    Unemployment_rate |    .021636   .0079901     2.71   0.007     .0059078    .0373641
       logReal_income |   .2043361   .0807633     2.53   0.012     .0453582     .363314
        real_interest |   .0992199   .0021356    46.46   0.000     .0950162    .1034236
log_PopulationDensity |   .0123006   .0189516     0.65   0.517    -.0250045    .0496057
           1.high_dev |  -.0133244   .0034886    -3.82   0.000    -.0201915   -.0064574
                      |
                 Year |
                2012  |   .0743276   .0057309    12.97   0.000     .0630466    .0856087
                2013  |   .0018383   .0090405     0.20   0.839    -.0159575    .0196341
                2014  |  -.1831882   .0070669   -25.92   0.000    -.1970989   -.1692775
                2015  |  -.1764396   .0073843   -23.89   0.000    -.1909752   -.1619039
                2016  |  -.1706576   .0051731   -32.99   0.000    -.1808407   -.1604746
                2017  |  -.0587763   .0029015   -20.26   0.000    -.0644876   -.0530649
                2018  |          0  (omitted)
                      |
                _cons |   9.917774   .8792601    11.28   0.000     8.187002    11.64855
----------------------+----------------------------------------------------------------
              sigma_u |  .21322018
              sigma_e |  .02499153
                  rho |    .986448   (fraction of variance due to u_i)
---------------------------------------------------------------------------------------

[Carlo Lazzaro]

Adam:
there's no way to fix it, as year 2018 is perfectly collinear with fixed effect.
In addition yo have a fantastic -within = 0.9240- and an apparent panel-wise effect.
No worries, then.

[Adam Klaas]

Adam:
there's no way to fix it, as year 2018 is perfectly collinear with fixed effect.
In addition yo have a fantastic -within = 0.9240- and an apparent panel-wise effect.
No worries, then.

Thank you so much sir for your helpful answer!