dropping year variable due to collinearity

[Fadi Ansar]

Dear all,
I have a panel data of 608 observations.
Whenever I am using i.year for both fixed effects(year) and ols model, the last year in my model is dropping due to collinearity .suppose if i remove year 2021 and consider year 2020, then also last year in my model is showing dropped due to collinearity.this only happening when lag_mappl variable is present in the model
Is there any issue in my over all result if it drops like that or anyway can i resolve this.

. reg lzscore loangrowth loangrowth_sq lasset lag_mappl efficiency ratioofnoninterestincome liquid
> ityratio capitalisation  i.year ,robust
note: 2021.year omitted because of collinearity.

Linear regression                               Number of obs     =        608
                                                F(22, 585)        =       5.65
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1329
                                                Root MSE          =     1.1766

------------------------------------------------------------------------------------------
                         |               Robust
                 lzscore | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
-------------------------+----------------------------------------------------------------
              loangrowth |   .0185964   .0054286     3.43   0.001     .0079344    .0292584
           loangrowth_sq |  -.0002277   .0000714    -3.19   0.002    -.0003679   -.0000874
                  lasset |   .1780924   .0491891     3.62   0.000     .0814836    .2747012
               lag_mappl |  -.1806264   .0537025    -3.36   0.001    -.2860996   -.0751532
              efficiency |   14.37221   12.52465     1.15   0.252    -10.22655    38.97097
ratioofnoninterestincome |  -.0320329   .0476933    -0.67   0.502    -.1257039     .061638
          liquidityratio |  -.0415131   .0234823    -1.77   0.078     -.087633    .0046067
          capitalisation |   2.977093   2.560367     1.16   0.245    -2.051538    8.005725
                         |
                    year |
                   2007  |  -.1345336   .2472446    -0.54   0.587    -.6201287    .3510615
                   2008  |   .2548396   .2646225     0.96   0.336    -.2648863    .7745656
                   2009  |    1.09878     .32077     3.43   0.001     .4687787    1.728781
                   2010  |   .2002277   .2773853     0.72   0.471    -.3445646    .7450201
                   2011  |   .1422808   .2768381     0.51   0.607    -.4014367    .6859983
                   2012  |  -.0837224   .2690855    -0.31   0.756    -.6122136    .4447688
                   2013  |   .5894946   .2808937     2.10   0.036     .0378117    1.141178
                   2014  |  -.0282862   .2478318    -0.11   0.909    -.5150347    .4584623
                   2015  |  -.0462421    .229075    -0.20   0.840    -.4961516    .4036675
                   2016  |   .6115729    .357973     1.71   0.088    -.0914959    1.314642
                   2017  |  -.0299619   .2846377    -0.11   0.916    -.5889982    .5290743
                   2018  |  -.6353101   .2703274    -2.35   0.019     -1.16624   -.1043797
                   2019  |  -.8127658   .2956974    -2.75   0.006    -1.393524   -.2320079
                   2020  |  -.2859594   .2657807    -1.08   0.282    -.8079601    .2360412
                   2021  |          0  (omitted)
                         |
                   _cons |  -.5370587   .7202829    -0.75   0.456    -1.951714    .8775966
------------------------------------------------------------------------------------------

any help is highly appreciated

[Clyde Schechter]

As you did not show example data, it is not possible to confidently give a precise answer to your problem.

The usual cause of this situation is that one of the variables in your regression is, or is equivalent to, an indicator for some subset of years. For example, if one of the variables in your model takes on a certain value during the years 2008-2012, and a different value in other years, or if one of your variables indicates years in which there was a recession, or something like that, you would lose one (or more) of your year indicators as a result. I cannot see in the names of your variables anything that looks like it has this property, but you would know better.

If this is not what is happening, to get better advice, please post back with example data that exhibits the problem. Make sure that the example data includes all of the variables in your -xtreg- command. And, to assure that the posted example data is usable for troubleshooting, be sure to use the -dataex- command to show it.

If you are running version 18, 17, 16 or a fully updated version 15.1 or 14.2, -dataex- is already part of your official Stata installation. If not, run -ssc install dataex- to get it. Either way, run -help dataex- to read the simple instructions for using it. -dataex- will save you time; it is easier and quicker than typing out tables. It includes complete information about aspects of the data that are often critical to answering your question but cannot be seen from tabular displays or screenshots. It also makes it possible for those who want to help you to create a faithful representation of your example to try out their code, which in turn makes it more likely that their answer will actually work in your data.

[Fadi Ansar]

Thanyou so much for your prompt reply.Here is my data .The version of stata I am using is stata17.

* Example generated by -dataex-. For more info, type help dataex
clear
input float(lzscore loangrowth loangrowth_sq lasset lag_mappl efficiency ratioofnoninterestincome liquidityratio capitalisation)
          .         .          . 10.717633 . .023704367  1.602717 .04899898 .05155952
  3.1504774  32.07006  1028.4888 10.920383 0  .01873243   .960711 .05292993 .06580575
   2.805421  34.82418  1212.7235 11.122306 0 .015181465   .612256 .05061003  .0661629
  2.8396385  18.57958   345.2008 11.325865 1  .01395699  1.281192 .05786476 .06295023
  1.4412208  16.77551  281.41776 11.489124 5 .014331458  1.264678 .04386888 .05992902
   1.746434   19.6991   388.0546 11.709308 1  .01329369  1.382194 .05011751 .05548889
   2.134561  26.81332   718.9542  11.92693 1 .015456133  1.004018 .05395706 .05623365
  2.7204185  17.15412  294.26382 12.116884 0 .014712303   .777138 .06362535 .05743369
  1.0046839  15.27643  233.36932 12.227703 4 .014474017   .762654 .05755587 .05554799
   .9233351   6.36997   40.57652 12.303355 1 .015681043  1.019639  .0596063   .053534
   .6240383   8.25129   68.08379  12.33313 2 .016355045   .892011 .07228242  .0556714
          .    1.6511  2.7261314 12.387667 5 .015322467  .8181782 .08846414 .05864283
   .2969247   -1.0684  1.1414787 12.375976 2 .017147437 1.1090522 .08872674 .06031272
   .6896611    .86393    .746375 12.440015 1  .01577523 1.0995603  .0135552 .04063241
   .7402934  -6.69603   44.83682 12.423503 0 .017877853  .6783315 .00698084 .03673051
  1.0264257    .52786  .27863616 12.460723 3 .018676525  .9699152  .0782312 .04908012
          .         .          . 10.396008 . .025343604   1.06693 .02826833 .26091126
   2.279583  23.24343    540.257  10.61323 0 .021095393  1.013228 .07411043 .05612817
  2.2455559  23.26384  541.20624 10.769348 0 .019629085  1.203198 .09081437 .07115781
  2.3824384  20.51125   420.7114  10.94363 1 .016063802  1.224009 .05877349 .06639069
  2.3195965  25.40026   645.1733  11.13414 5   .0161278  1.214798 .06303038 .05741565
  2.2156472  24.00265   576.1272 11.411362 1 .014938003   .900365 .04855341 .05326472
   2.150822   24.1416  582.81683 11.598192 1 .015655214   .736726 .08966737 .04881476
   2.450127  15.77511   248.8541 11.732426 0  .01448668   .773451 .07149027 .05961776
  1.8757025  16.22257  263.17178 11.893407 4 .013924973   .849919 .05166451 .06005353
  .06643533   9.00621   81.11182 12.027788 1 .013803767   .850947 .03459445 .05769821
  .13149223  15.70911  246.77614  12.12903 2 .014794166  .8124353 .04721202 .05221348
          .   3.76539  14.178162  12.20588 5 .014629976 1.0912397 .03435137 .05435045
 -.07693692   4.52814  20.504053 12.315372 2 .015472895  1.019648  .0370496 .05498245
   .7019485   8.55188   73.13465   12.3974 1 .013815789  .8321921 .07113651 .05094886
   .4699301   6.34117   40.21043 12.426458 0 .014961805 1.1123437 .00272063 .04467204
  .12771104   -.68254   .4658608 12.404395 3   .0190225  1.343654 .00237419 .05280597
          .         .          . 10.538573 .  .01540334  1.668216 .03176395 .05014084
   4.066645  35.77662  1279.9664 10.814386 0 .016369041  1.642613 .11930302 .06415912
   3.003784  50.23499   2523.554 11.201732 0 .016579915  1.964053 .07323065 .05802455
   2.933533  48.11068  2314.6375  11.60439 1 .019665716  2.251752 .09443855 .04644193
   2.178339  31.26183    977.302 11.903088 5 .019348586  2.403254 .11411285 .08004068
  2.1009483  24.63646  606.95514 12.104305 1  .02053566  2.188266 .10165643 .06914881
   2.561132  31.10313   967.4047 12.399636 1 .019691655  2.051786 .08416324  .0888171
   5.719707  17.56878  308.66205 12.562445 0 .021031216  2.092375  .0882055 .07827681
    4.99326  14.86483  220.96317  12.73835 4 .020302514  2.046192 .04878347 .07985406
  3.5168095  15.53383  241.29987  12.85643 1  .02061547  1.979477 .06000388 .09721574
  3.3803875  20.02811   401.1252 13.043174 2 .019924443 1.8710122 .07368316 .09972863
          .  18.66817   348.5006 13.198993 5 .018711424 2.0487912 .07814787 .09671658
-.018533692   9.64317   92.99074 13.307128 2  .02028356 1.6966444 .06173424 .09848618
 -1.8646643   16.4216  269.66895 13.446372 1  .02023686 1.7597145 .08355592 .09271078
   .7217803  11.81698  139.64102 13.593612 0  .01976714 1.8106177 .06285698  .0917728
 -.04114822  14.39819  207.30786  13.72686 3  .01890875      1.29 .08390128 .08324168
  1.0871499   7.25136   52.58222  13.80222 7  .01862099  1.407859 .10150822 .09282245
          .         .          . 11.458092 . .020939125   1.45164  .2134315 .19210494
   2.961022   7.56459   57.22302  11.63861 0  .02103095  1.083733 .06464861 .05944964
  1.1321929  23.36373  545.86383  11.87162 0 .017774234  1.077259 .07491468 .06917949
   1.091211  47.66164  2271.6318 12.098485 1 .016894767  1.270992  .0806467  .0604273
   1.804965  -1.65024   2.723292  12.33126 5 .015776355  1.357544 .08012199 .06149199
    3.33441    .74472  .55460787 12.536515 1  .01369153  1.111453 .06720789 .05682091
     3.5079  37.73012   1423.562 12.789397 1 .012918168   .882401 .07928763 .05427766
   .1946073  12.62445  159.37672 13.011033 0  .01153246   .849509 .09126455 .05871567
  1.3729702  22.40591   502.0248 13.212452 4 .010868856   .730172  .0972981 .06142528
  1.3166246  47.41678  2248.3508 13.399244 1  .01082186   .739698 .11944094 .05843058
  1.6536114  13.29737  176.82005 13.480021 2 .010733217   .640526 .19493967 .05456471
   2.958964  34.20153  1169.7446 13.417086 5 .013290809  .7211465 .21349604 .05571466
  1.9521568 -12.05645  145.35799 13.451488 2 .013378516  .9892848 .18597277 .05987546
  2.2807143   8.21476   67.48228 13.487006 1 .014129685  .9410233 .19266625 .05800069
    2.92383 -25.88949   670.2657 13.568315 0  .01445347   .838714 .01656972 .06027053
   .5731756    .50783  .25789133 13.962132 3  .01629859  .9211959 .01497981 .05882438
  1.1715125 -35.55574  1264.2107 13.959927 7   .0177811     1.118 .05275442 .06205653
          .         .          . 11.461403 .  .02034493  1.142934 .10105927   .466394
 -.17294066  16.55717   274.1399   11.6287 0 .018839024   1.23023 .05307016 .04700944
   1.044165  -9.67116   93.53133 11.862292 0 .018392934  1.320409 .06400484 .04439028
   .9663768 -11.60526  134.68205  12.09419 1 .014790514  1.509584  .0771858  .0415703
   .7719781 -22.19951   492.8183 12.326083 5  .01372035  1.045675 .06443473 .05921484
  -.3382419  -5.81717   33.83947 12.524405 1  .01333913    .84383 .06196742 .05984396
 .017029136  44.46199  1976.8685 12.769033 1 .014432332   .902851 .08121522 .05175172
  2.0316763  19.32116   373.3072 12.859792 0  .01284838   .899742  .0824343 .04923698
   2.833213   21.1103   445.6448  13.02277 4  .01177975   .836785   .060558 .05451198
   2.536537  47.89357   2293.794 13.258973 1 .011688037   .704412  .0886311 .05284582
  1.7777258   30.2361   914.2217 13.335372 2 .013073567  .5945799  .1006409 .05220445
  2.2546787    11.131  123.89915 13.321074 5 .015316164 1.0956485 .11604526  .0508273
    .848083  19.44261   378.0151   13.3476 2 .014155622  .9278795  .1626401  .0508491
   .9331604  15.77611  248.88564 13.320517 1 .014930357  .7545995 .15336604 .04912135
  1.8652022  -13.1803   173.7203 13.345863 0 .016353128 1.0471032 .06297007 .05830399
  1.8444024     11.59  134.32811 13.395432 3 .015907874       .99 .04979629 .06666607
   .8681311  20.08867   403.5547 13.495108 7  .01493285  1.078931 .07778045 .06669196
          .         .          . 10.400767 .  .02190777   .163009  .0673022  .0750479
  1.2281214  15.14807  229.46404 10.348639 0   .0211031   .754882 .09929221 .04690353
  1.2979954  26.48401   701.4028  10.57156 0 .019124905   .872617  .0496093 .05036581
   1.510084  28.96976   839.2471 10.782095 1 .017369118   .933037 .05660451  .0446529
   1.618085  23.06156   531.8355 10.985807 5 .016313951   .909004  .0594735 .03699832
   .6184936    16.467  271.16205  11.17122 1 .015100065   .719817 .05370508 .04264226
   .7138454  23.48633   551.6077  11.24429 1  .02150937   .764611 .07345566  .0402277
  2.4888134  15.50398   240.3734 11.420123 0 .018022409   .876544 .03462385 .05194676
  2.0506687  15.09142  227.75096 11.669526 4 .015362117   .706108 .04213801 .05181912
   1.587159  24.77856   613.9771  11.82276 1 .017581778   .712605 .04227432 .05469675
   .5124683   8.10328   65.66315  11.89149 2 .017298667   .664084 .03791252 .05405034
  1.0712051 -11.26671  126.93876 11.988894 5 .015860165  .9417188 .04238605  .0552486
   -.313985   2.00996   4.039939 11.978695 2 .017923824   .954248 .05961891 .05459301
   .8947146  -7.09515   50.34115 11.959717 1 .017299363  .9645538 .10114545 .04631772
    .900919   -.10967 .012027509  12.01088 0  .01873959  .9893301 14.214532 .06360928
   .6500078   7.85799     61.748 12.036868 3 .018244876     1.438  4.967009 .03488269
   .1680718   -.87032    .757457 12.189257 7  .01812756  1.241575 .06163558 .06369076
end

[Fadi Ansar]

I have a bank-level data and my lag_mappl variable is a country-level variable, is it possible reason why I am getting a result like this? When I remove this variable then none of the year variables is dropped. In such a case is there any way I can capture year fixed effects?

[Clyde Schechter]

I can't do anything with this example because it does not contain the year variable. Please try again, and be sure to include the year variable along with the others.

[Fadi Ansar]

I have incorporated year variable as well.

* Example generated by -dataex-. For more info, type help dataex
clear
input float(lzscore loangrowth loangrowth_sq lasset lag_mappl efficiency ratioofnoninterestincome liquidityratio capitalisation) int year
          .         .          . 10.717633 . .023704367  1.602717 .04899898 .05155952 2005
  3.1504774  32.07006  1028.4888 10.920383 0  .01873243   .960711 .05292993 .06580575 2006
   2.805421  34.82418  1212.7235 11.122306 0 .015181465   .612256 .05061003  .0661629 2007
  2.8396385  18.57958   345.2008 11.325865 1  .01395699  1.281192 .05786476 .06295023 2008
  1.4412208  16.77551  281.41776 11.489124 5 .014331458  1.264678 .04386888 .05992902 2009
   1.746434   19.6991   388.0546 11.709308 1  .01329369  1.382194 .05011751 .05548889 2010
   2.134561  26.81332   718.9542  11.92693 1 .015456133  1.004018 .05395706 .05623365 2011
  2.7204185  17.15412  294.26382 12.116884 0 .014712303   .777138 .06362535 .05743369 2012
  1.0046839  15.27643  233.36932 12.227703 4 .014474017   .762654 .05755587 .05554799 2013
   .9233351   6.36997   40.57652 12.303355 1 .015681043  1.019639  .0596063   .053534 2014
   .6240383   8.25129   68.08379  12.33313 2 .016355045   .892011 .07228242  .0556714 2015
          .    1.6511  2.7261314 12.387667 5 .015322467  .8181782 .08846414 .05864283 2016
   .2969247   -1.0684  1.1414787 12.375976 2 .017147437 1.1090522 .08872674 .06031272 2017
   .6896611    .86393    .746375 12.440015 1  .01577523 1.0995603  .0135552 .04063241 2018
   .7402934  -6.69603   44.83682 12.423503 0 .017877853  .6783315 .00698084 .03673051 2019
  1.0264257    .52786  .27863616 12.460723 3 .018676525  .9699152  .0782312 .04908012 2020
          .         .          . 10.396008 . .025343604   1.06693 .02826833 .26091126 2005
   2.279583  23.24343    540.257  10.61323 0 .021095393  1.013228 .07411043 .05612817 2006
  2.2455559  23.26384  541.20624 10.769348 0 .019629085  1.203198 .09081437 .07115781 2007
  2.3824384  20.51125   420.7114  10.94363 1 .016063802  1.224009 .05877349 .06639069 2008
  2.3195965  25.40026   645.1733  11.13414 5   .0161278  1.214798 .06303038 .05741565 2009
  2.2156472  24.00265   576.1272 11.411362 1 .014938003   .900365 .04855341 .05326472 2010
   2.150822   24.1416  582.81683 11.598192 1 .015655214   .736726 .08966737 .04881476 2011
   2.450127  15.77511   248.8541 11.732426 0  .01448668   .773451 .07149027 .05961776 2012
  1.8757025  16.22257  263.17178 11.893407 4 .013924973   .849919 .05166451 .06005353 2013
  .06643533   9.00621   81.11182 12.027788 1 .013803767   .850947 .03459445 .05769821 2014
  .13149223  15.70911  246.77614  12.12903 2 .014794166  .8124353 .04721202 .05221348 2015
          .   3.76539  14.178162  12.20588 5 .014629976 1.0912397 .03435137 .05435045 2016
 -.07693692   4.52814  20.504053 12.315372 2 .015472895  1.019648  .0370496 .05498245 2017
   .7019485   8.55188   73.13465   12.3974 1 .013815789  .8321921 .07113651 .05094886 2018
   .4699301   6.34117   40.21043 12.426458 0 .014961805 1.1123437 .00272063 .04467204 2019
  .12771104   -.68254   .4658608 12.404395 3   .0190225  1.343654 .00237419 .05280597 2020
          .         .          . 10.538573 .  .01540334  1.668216 .03176395 .05014084 2005
   4.066645  35.77662  1279.9664 10.814386 0 .016369041  1.642613 .11930302 .06415912 2006
   3.003784  50.23499   2523.554 11.201732 0 .016579915  1.964053 .07323065 .05802455 2007
   2.933533  48.11068  2314.6375  11.60439 1 .019665716  2.251752 .09443855 .04644193 2008
   2.178339  31.26183    977.302 11.903088 5 .019348586  2.403254 .11411285 .08004068 2009
  2.1009483  24.63646  606.95514 12.104305 1  .02053566  2.188266 .10165643 .06914881 2010
   2.561132  31.10313   967.4047 12.399636 1 .019691655  2.051786 .08416324  .0888171 2011
   5.719707  17.56878  308.66205 12.562445 0 .021031216  2.092375  .0882055 .07827681 2012
    4.99326  14.86483  220.96317  12.73835 4 .020302514  2.046192 .04878347 .07985406 2013
  3.5168095  15.53383  241.29987  12.85643 1  .02061547  1.979477 .06000388 .09721574 2014
  3.3803875  20.02811   401.1252 13.043174 2 .019924443 1.8710122 .07368316 .09972863 2015
          .  18.66817   348.5006 13.198993 5 .018711424 2.0487912 .07814787 .09671658 2016
-.018533692   9.64317   92.99074 13.307128 2  .02028356 1.6966444 .06173424 .09848618 2017
 -1.8646643   16.4216  269.66895 13.446372 1  .02023686 1.7597145 .08355592 .09271078 2018
   .7217803  11.81698  139.64102 13.593612 0  .01976714 1.8106177 .06285698  .0917728 2019
 -.04114822  14.39819  207.30786  13.72686 3  .01890875      1.29 .08390128 .08324168 2020
  1.0871499   7.25136   52.58222  13.80222 7  .01862099  1.407859 .10150822 .09282245 2021
          .         .          . 11.458092 . .020939125   1.45164  .2134315 .19210494 2005
   2.961022   7.56459   57.22302  11.63861 0  .02103095  1.083733 .06464861 .05944964 2006
  1.1321929  23.36373  545.86383  11.87162 0 .017774234  1.077259 .07491468 .06917949 2007
   1.091211  47.66164  2271.6318 12.098485 1 .016894767  1.270992  .0806467  .0604273 2008
   1.804965  -1.65024   2.723292  12.33126 5 .015776355  1.357544 .08012199 .06149199 2009
    3.33441    .74472  .55460787 12.536515 1  .01369153  1.111453 .06720789 .05682091 2010
     3.5079  37.73012   1423.562 12.789397 1 .012918168   .882401 .07928763 .05427766 2011
   .1946073  12.62445  159.37672 13.011033 0  .01153246   .849509 .09126455 .05871567 2012
  1.3729702  22.40591   502.0248 13.212452 4 .010868856   .730172  .0972981 .06142528 2013
  1.3166246  47.41678  2248.3508 13.399244 1  .01082186   .739698 .11944094 .05843058 2014
  1.6536114  13.29737  176.82005 13.480021 2 .010733217   .640526 .19493967 .05456471 2015
   2.958964  34.20153  1169.7446 13.417086 5 .013290809  .7211465 .21349604 .05571466 2016
  1.9521568 -12.05645  145.35799 13.451488 2 .013378516  .9892848 .18597277 .05987546 2017
  2.2807143   8.21476   67.48228 13.487006 1 .014129685  .9410233 .19266625 .05800069 2018
    2.92383 -25.88949   670.2657 13.568315 0  .01445347   .838714 .01656972 .06027053 2019
   .5731756    .50783  .25789133 13.962132 3  .01629859  .9211959 .01497981 .05882438 2020
  1.1715125 -35.55574  1264.2107 13.959927 7   .0177811     1.118 .05275442 .06205653 2021
          .         .          . 11.461403 .  .02034493  1.142934 .10105927   .466394 2005
 -.17294066  16.55717   274.1399   11.6287 0 .018839024   1.23023 .05307016 .04700944 2006
   1.044165  -9.67116   93.53133 11.862292 0 .018392934  1.320409 .06400484 .04439028 2007
   .9663768 -11.60526  134.68205  12.09419 1 .014790514  1.509584  .0771858  .0415703 2008
   .7719781 -22.19951   492.8183 12.326083 5  .01372035  1.045675 .06443473 .05921484 2009
  -.3382419  -5.81717   33.83947 12.524405 1  .01333913    .84383 .06196742 .05984396 2010
 .017029136  44.46199  1976.8685 12.769033 1 .014432332   .902851 .08121522 .05175172 2011
  2.0316763  19.32116   373.3072 12.859792 0  .01284838   .899742  .0824343 .04923698 2012
   2.833213   21.1103   445.6448  13.02277 4  .01177975   .836785   .060558 .05451198 2013
   2.536537  47.89357   2293.794 13.258973 1 .011688037   .704412  .0886311 .05284582 2014
  1.7777258   30.2361   914.2217 13.335372 2 .013073567  .5945799  .1006409 .05220445 2015
  2.2546787    11.131  123.89915 13.321074 5 .015316164 1.0956485 .11604526  .0508273 2016
    .848083  19.44261   378.0151   13.3476 2 .014155622  .9278795  .1626401  .0508491 2017
   .9331604  15.77611  248.88564 13.320517 1 .014930357  .7545995 .15336604 .04912135 2018
  1.8652022  -13.1803   173.7203 13.345863 0 .016353128 1.0471032 .06297007 .05830399 2019
  1.8444024     11.59  134.32811 13.395432 3 .015907874       .99 .04979629 .06666607 2020
   .8681311  20.08867   403.5547 13.495108 7  .01493285  1.078931 .07778045 .06669196 2021
          .         .          . 10.400767 .  .02190777   .163009  .0673022  .0750479 2005
  1.2281214  15.14807  229.46404 10.348639 0   .0211031   .754882 .09929221 .04690353 2006
  1.2979954  26.48401   701.4028  10.57156 0 .019124905   .872617  .0496093 .05036581 2007
   1.510084  28.96976   839.2471 10.782095 1 .017369118   .933037 .05660451  .0446529 2008
   1.618085  23.06156   531.8355 10.985807 5 .016313951   .909004  .0594735 .03699832 2009
   .6184936    16.467  271.16205  11.17122 1 .015100065   .719817 .05370508 .04264226 2010
   .7138454  23.48633   551.6077  11.24429 1  .02150937   .764611 .07345566  .0402277 2011
  2.4888134  15.50398   240.3734 11.420123 0 .018022409   .876544 .03462385 .05194676 2012
  2.0506687  15.09142  227.75096 11.669526 4 .015362117   .706108 .04213801 .05181912 2013
   1.587159  24.77856   613.9771  11.82276 1 .017581778   .712605 .04227432 .05469675 2014
   .5124683   8.10328   65.66315  11.89149 2 .017298667   .664084 .03791252 .05405034 2015
  1.0712051 -11.26671  126.93876 11.988894 5 .015860165  .9417188 .04238605  .0552486 2016
   -.313985   2.00996   4.039939 11.978695 2 .017923824   .954248 .05961891 .05459301 2017
   .8947146  -7.09515   50.34115 11.959717 1 .017299363  .9645538 .10114545 .04631772 2018
    .900919   -.10967 .012027509  12.01088 0  .01873959  .9893301 14.214532 .06360928 2019
   .6500078   7.85799     61.748 12.036868 3 .018244876     1.438  4.967009 .03488269 2020
   .1680718   -.87032    .757457 12.189257 7  .01812756  1.241575 .06163558 .06369076 2021
end

[Clyde Schechter]

Well, thank you for discovering the culprit variable, lag_mappl and saving me the time. If you run

. tabstat lag_mappl, by(year) statistics(min max)

Summary for variables: lag_mappl
Group variable: year

    year |       Min       Max
---------+--------------------
    2005 |         .         .
    2006 |         0         0
    2007 |         0         0
    2008 |         1         1
    2009 |         5         5
    2010 |         1         1
    2011 |         1         1
    2012 |         0         0
    2013 |         4         4
    2014 |         1         1
    2015 |         2         2
    2016 |         5         5
    2017 |         2         2
    2018 |         1         1
    2019 |         0         0
    2020 |         3         3
    2021 |         7         7
---------+--------------------
   Total |         0         7
------------------------------

you can see that within any year, lag_mappl does not vary at all. In 2006, lag_mappl is always 0. Same in 2007. In 2008 it is always 1, and in 2009 it is always 5, etc. So lag_mappl, although you describe it as a country variable, it is in fact a proxy variable for year. That means that you cannot have lag_mappl in the model and also get all of the years. Either remove lag_mappl, or accept a model with incomplete year variables. It's your choice, but there's no way out of it.

[Fadi Ansar]

Thanks for your great help. The reason why this lag_mappl is not changing because this data set is for a single country and all my other observations are in bank-level variables. This is the only variable which is a country-level variable.