Hi all,

I'm running a series of regression with industry and year fixed effect
My dependent variable is a proxy for manipulation of earnings. My key variable of interest is a dummy (quint1) that doesn't change over time and after I have a series of controls.
Since I have several proxies of manipulation of earnings I'm running multiple regression.
I'm struggling to understand why, for some of the regressions, I have this output.

Code:
reg absmoddd quintB1 aver_shareturn analysts_log logat salevolatility cashvolatility workcapit proploss delsalegrowth earn futearn ppe deltaWRC changesale cfo accr bigaud roa i.fyear i.ffind, cluster(gvkey)
note: 2016.fyear omitted because of collinearity
note: 2017.fyear omitted because of collinearity
note: 2018.fyear omitted because of collinearity

Linear regression                               Number of obs     =     38,454
                                                F(54, 5145)       =          .
                                                Prob > F          =          .
                                                R-squared         =     0.5437
                                                Root MSE          =     .08192

                               (Std. Err. adjusted for 5,146 clusters in gvkey)
-------------------------------------------------------------------------------
              |               Robust
     absmoddd |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
      quintB1 |   .0021172   .0063768     0.33   0.740     -.010384    .0146185
aver_sharet~n |   4.83e-08   4.07e-09    11.86   0.000     4.03e-08    5.63e-08
 analysts_log |  -.0030811   .0005967    -5.16   0.000    -.0042508   -.0019114
        logat |  -.0055064   .0003689   -14.93   0.000    -.0062296   -.0047832
salevolatil~y |   1.110494          .        .       .            .           .
cashvolatil~y |   2.592445          .        .       .            .           .
    workcapit |   .6596937          .        .       .            .           .
     proploss |  -3.900873          .        .       .            .           .
delsalegrowth |   .0014231   .0008765     1.62   0.105    -.0002953    .0031414
         earn |  -.2315857   .0174807   -13.25   0.000    -.2658552   -.1973161
      futearn |  -.0102936   .0073057    -1.41   0.159    -.0246158    .0040287
          ppe |  -.0255437   .0037311    -6.85   0.000    -.0328583   -.0182291
     deltaWRC |   .1210259   .1170141     1.03   0.301    -.1083714    .3504233
   changesale |   .0291029   .0031282     9.30   0.000     .0229703    .0352355
          cfo |   .1973238   .0143676    13.73   0.000     .1691573    .2254903
         accr |   .8036271   .2924855     2.75   0.006     .2302311    1.377023
       bigaud |   -.182379          .        .       .            .           .
          roa |  -.0468239   .0108017    -4.33   0.000    -.0679998   -.0256479
              |
        fyear |
        2002  |   .0684815          .        .       .            .           .
        2003  |   .0275448          .        .       .            .           .
        2004  |  -.0781455          .        .       .            .           .
        2005  |  -.1743677          .        .       .            .           .
        2006  |  -.3005914          .        .       .            .           .
        2007  |  -.3505113          .        .       .            .           .
        2008  |  -.2788789          .        .       .            .           .
        2009  |  -.1475632          .        .       .            .           .
        2010  |  -.0795899          .        .       .            .           .
        2011  |  -.0488357          .        .       .            .           .
        2012  |  -.0239605          .        .       .            .           .
        2013  |  -.0673017          .        .       .            .           .
        2014  |   -.136407          .        .       .            .           .
        2015  |  -.0694582          .        .       .            .           .
        2016  |          0  (omitted)
        2017  |          0  (omitted)
        2018  |          0  (omitted)
              |
        ffind |
           2  |   .0165133   .0038744     4.26   0.000     .0089178    .0241088
           3  |   .0061173   .0060999     1.00   0.316     -.005841    .0180757
           4  |   .0137559   .0068625     2.00   0.045     .0003026    .0272092
           6  |   .0261899   .0050341     5.20   0.000      .016321    .0360588
           7  |   .0271067   .0040535     6.69   0.000     .0191601    .0350534
           8  |   .0307849   .0091985     3.35   0.001      .012752    .0488178
           9  |   .0121585   .0038337     3.17   0.002     .0046428    .0196742
          10  |   .0129211   .0041018     3.15   0.002     .0048799    .0209624
          11  |   .0387873   .0049461     7.84   0.000     .0290907    .0484838
          12  |   .0333732   .0042868     7.79   0.000     .0249692    .0417772
          13  |   .0778667   .0053883    14.45   0.000     .0673034    .0884301
          14  |   .0248361    .007387     3.36   0.001     .0103544    .0393177
          15  |   .0124818   .0077165     1.62   0.106    -.0026458    .0276094
          16  |   .0252366   .0113663     2.22   0.026     .0029538    .0475195
          17  |   .0180586   .0073686     2.45   0.014     .0036129    .0325043
          18  |   .0174014   .0080347     2.17   0.030       .00165    .0331528
          19  |   .0161763   .0074397     2.17   0.030     .0015914    .0307612
          20  |  -.0102544   .0102338    -1.00   0.316     -.030317    .0098081
          21  |   .0195901   .0073257     2.67   0.008     .0052287    .0339515
          22  |   .0173262   .0078094     2.22   0.027     .0020165    .0326359
          23  |    .021891   .0074394     2.94   0.003     .0073066    .0364754
          24  |   .0132493     .00759     1.75   0.081    -.0016303    .0281289
          25  |   .0011462   .0076273     0.15   0.881    -.0138066     .016099
          26  |   .0048563   .0175949     0.28   0.783    -.0296372    .0393498
          27  |   .0379749   .0078397     4.84   0.000     .0226057     .053344
          28  |   .0451368   .0079863     5.65   0.000     .0294803    .0607933
          29  |   .0075163   .0079214     0.95   0.343    -.0080129    .0230455
          30  |   .0469983   .0075624     6.21   0.000     .0321727    .0618239
          33  |   .0141865   .0074641     1.90   0.057    -.0004464    .0288194
          34  |   .0422195   .0072676     5.81   0.000     .0279718    .0564671
          35  |   .0408326   .0075694     5.39   0.000     .0259933    .0556719
          36  |   .0478678   .0073668     6.50   0.000     .0334258    .0623099
          37  |   .0174294    .007406     2.35   0.019     .0029105    .0319483
          38  |   .0110031   .0074203     1.48   0.138    -.0035438    .0255501
          39  |   .0037448   .0075109     0.50   0.618    -.0109796    .0184693
          40  |   .0172039   .0082013     2.10   0.036     .0011259     .033282
          41  |   .0172645     .00734     2.35   0.019      .002875     .031654
          42  |   .0130322   .0072594     1.80   0.073    -.0011993    .0272637
          43  |   .0102852   .0077753     1.32   0.186    -.0049577    .0255281
          46  |   .0520101   .0101473     5.13   0.000     .0321171     .071903
          47  |   .0435883   .0092769     4.70   0.000     .0254015     .061775
-------------------------------------------------------------------------------
It is clear that some of the year dummies are omitted thanks to multicollinearity.
But why do I have a series of dots between year dummies 2002 and 2015 for the columns of std error, p statistcs etc.?
The same problem happens also for three controls (cashvolatility, salesvolatility, proprloss)
Do you have any suggestions?
Thanks