Predicted Residual is not clearning from memory

[Karim Swopon]

Hi

I am trying to predict the residual after regression. But the predicted residual from the previous regression is not clearing from the memory, and it shows an error that the variable is already defined. Even though I dropped the variable and cleared the estimates and results. Below is the codes:

clear results
estimate clear
ereturn list

levelsof Fama_French, local(industries)

foreach Fama_French of local industries {
    qui levelsof year if Fama_French == `Fama_French', local(years)
    
    foreach year of local years {
        qui count if Fama_French == `Fama_French' & year == `year'
        local obs = r(N)

        if `obs' >= 20 {
            qui reg TCA_W CFOt_1_W CFOt_W CFOt1_W C_Rev_W PPE_W if Fama_French == `Fama_French' & year == `year', robust
            
            predict double AQ, residuals 
            }
    }
}

Error:

(51,222 missing values generated)
variable AQ already defined
r(110);

* Example generated by -dataex-. For more info, type help dataex
clear
input float(PPE_W CFOt_W CFOt1_W CFOt_1_W TCA_W C_Rev_W)
  -5.21567 -1.5112712 -1.0715939          .          .           .
  -2.65431  -.4415217  -.3830536  -.6226791 .019210204  -1.7223364
 25.682127   3.207224   5.545249  3.6967645   3.083213   10.168536
-24.948883  -5.204473  -4.466454  -3.010128  -.3389456  -2.9585304
  2.447334   .4084733 -.12563916  .47596785   .1154127   -.3859752
 19.026062 -1.1796982   .8806584   3.835391    -3.8107  -12.090535
  -23.5871 -1.0354838  -.7032258  1.3870968   1.016129  -2.2032259
-75.281555 -2.1165047  -6.242719  -3.116505  -1.961165  -10.194175
     141.6  17.146667  17.973333   5.813333   8.853333   -5.146667
-2.0669277 -.23994306  .12851548  -.2289071 .016375937  -1.0804557
  2.535138 -.13387725  .39013535  .24995366   .3371037   .13906917
-37.444443  -6.152047 -4.3976607   2.111111   6.122808   25.608187
         .          .          .          .          .           .
 -42.65585  -21.44781  -22.02254          .          .           .
 -5.178624 -2.5783305  -2.677302  -2.511043   .8482286  -1.0590719
 1.5590916   .7671875  .27378368   .7388269   .3937432    .0842314
  15.33969  3.9618595  4.3802147   11.10179 -.22992358  -21.484995
 305.26315   58.81108   60.55072   59.65454  -38.67117   -51.45112
-2.1688414 -.51948506  -.5609948  -.6436641   .3511691 -.027673176
-1.0486771 -.27162483  -.4723541 -.25152645   .0234905  -.03799186
  3.363446  1.2272778   1.388124   .7057398 -.06147406   1.4403437
  -43.9944 -17.647058 -17.187675  -15.60224  1.5658263   -8.929972
 -7.609097 -2.6329114 -3.3902595 -2.7032826 -.27633554  -.56897664
 -15.02568  -5.967523  -7.955438  -4.634441   .3549849  -2.0422962
  54.13456   27.79156  25.279684  20.846966  .46701846    22.34037
         .          .          .          .          .           .
 1.1264602 -.29258355  -.1764466          .          .           .
         .          .          .          .          .           .
  .1788618 -2.3252032  -9.300813          .          .           .
 .52380955 -27.238096  -24.66667  -6.809524  16.190477  -19.809525
-.24175824  11.384616    24.1978   12.57143  1.7142856           0
         0  -7.339999  -.6333333  -3.453333 -1.9866667           0
         0        -68 -66.217766 -68.180984  -38.67117           0
         0  14.328358   20.32836   2.835821  1.8805977           0
         0  14.967033   5.362638   10.54945  -5.186813           0
         0  .13326052  1.0322229   .3719279  .13489898           0
         0 -1.8243243 -1.2760618 -.23552124   .6544401           0
         0  -2.443623         -2 -3.4935305   .8724584           0
         0  -5.303922  -6.970589  -6.480392   .3627448           0
         0 -10.304348  -6.014493   -7.84058 -1.0289855           0
         .          .          .          .          .           .
 -.3369334    .388378  -.3517678          .          .           .
  2.792249  2.2669358  2.1427534  -2.502866   .4744449   10.625272
 -1.471801  -1.098882 -1.9653003 -1.1625673 -.59354043   -2.646791
-.25779983  -.3283341  -.3466663 -.18358538 -.09265739   -.7651396
-1.9002293 -2.2997704  -.7657641 -2.1781552 -1.5053694   -.9627352
 -.7845659  -.3271508 -1.2481766  -.9825112 -1.4983138   -1.441926
 -.0832638 -.12798774  -.1791973 -.03354597  -.1030826  -.04653332
 .15898304   .3287125   .7508897  .23477563   .1505526    .5885359
         .          .          .          .          .           .
  6.365853  -1.386418  -.5251076          .          .           .
  .5241773  -.1843519  -.6279382  -.4867361          .  -.27132306
 1.7057302  -.9217499 -1.2362292    -.27061          .    -.109427
  7.183027  -5.128834  -1.311861  -3.824131          .   -.4662577
  5.925688 -1.1770642  -.5844037 -4.6018343          .   -.4082569
  10.23632 -1.0103093  2.0919905 -2.0348928          .  -1.9746233
  2.184144    .444033 -1.0728834   -.214442          .   .12859789
  2.387061 -1.1714759  .25877595  .48483735          .  -.19775777
  5.517797   .5966102 -1.1093221 -2.7008476          .   -.8050848
 16.789133 -3.3868046  -.5743855  1.8214748          .    3.249677
-18.254496   .6141079   .6556017   3.621024          .    2.724758
 1.8374982 -.06617339 -.55172414  -.0619852          .    .0795756
         .          .          .          .          .           .
-4.0783553   .4024109  -.7314306          .          .           .
-1.6217674 -.32730445   .2019673    .180073 -.50071394    -1.61082
 -86.59761  10.143427  -51.59363 -16.438248  -38.67117   -51.45112
 -2.735061 -1.1532638  -.8280346  .22673434   .2605753     -.92742
-14.793185 -4.4003787 -1.0222434  -6.128727 -4.1609087  -16.701374
-3.0228276 -.20291217 -1.6640676  -.8734617 -1.0164396   -.4866134
-1.7216733   -.781592  -.4245499 -.09530534 -.12883869  -1.3026828
  10.36855  1.8317152  1.7037884  3.3721685  1.9920044   2.2752714
 -46.87265  -7.036164  -23.11635  -7.564466  -5.536163  -10.646227
-3.5167906  -1.538027  -.5984936  -.4681452   .4634376   -.5372947
 -5.711639  -.7852045  -.7055998 -2.0178425   -1.29042   -2.957178
 -4.825659  -.5156987  -.2900993 -.57387906 -1.1099408  -1.8141237
         .          .          .          .          .           .
-3.6560776  -.9525505  -1.439737          .          .           .
 -8.659256 -2.7453825  -2.680406 -1.8163843 -.04225166   -49.30666
 -13.79343 -3.8133025  -3.077289 -3.9057415  .03988668  -11.655645
   3.96079   .8199277   .8787896  1.0160346  .14157064   10.534456
 4.4662547   .9658501    .522075   .9011568  -.3416019    3.829076
  -116.618 -13.933278  -3.797648 -25.776865 -1.2605968     47.1314
-10.641214  -.3875865  -1.456575  -1.422025  -.8170909     47.1314
  65.87167  13.612937  16.178404  3.6223264  -6.296296     47.1314
 -5.366004  -1.193489 -1.8047794 -1.0042331   .6500424  -16.476295
 10.031454  2.7066224 -2.5392585  1.7898717  -.6329824    3.522378
 -5.021983  1.1207652  -2.063936 -1.1946355 -1.5452136   20.093357
 -43.73012 -17.137478  -2.913917   9.306048   9.437182   -51.45112
         .          .          .          .          .           .
  3.390832 -2.1073005 -.09643464          .          .           .
 -4.929484  .13440607  -4.357785   2.937056  -1.968765    1.751065
 -177.2381        -68   -63.1746  4.5079365  -9.523816   -51.45112
   -3.9811 -1.3432332  -2.680391  -3.107661 -.43536955    .3732703
 -5.346325  -3.224523  2.3840845 -1.6159155  .17702003   -3.205846
 -4.880775  1.8972536  -6.927948 -2.5660744 -1.1121162    .8071082
 -69.57198        -68  17.789883   22.84825  36.183453   -51.45112
  5.203324  -1.266482    2.92964   5.939612   3.894737   -6.257064
  17.21183   8.454037   -3.97442  -3.654676  -15.69944   15.963228
  71.32001  -11.04889   19.75111  23.502224   2.560001   -41.24445
 -2.417216  -.6892594   .9774331   .3855758  .21015897   -.6264443
end

[Carlo Lazzaro]

Karim:
welcome to this forum.
You may want to try something alobg the following lines:

. forvalues i = 70(1)73 {
  2. quietly regress ln_wage c.age##c.age if year==`i'
  3. predict d, residuals
  4. g res_`i'=d
  5. drop d
  6. }

[Karim Swopon]

Hi Carlo

Thank you very much for your help. I have edited the code as you suggested. Below is the edited code:

clear results
estimate clear
ereturn list

levelsof Fama_French, local(industries)

foreach Fama_French of local industries {
    qui levelsof year if Fama_French == `Fama_French', local(years)
    
    foreach year of local years {
        qui count if Fama_French == `Fama_French' & year == `year'
        local obs = r(N)

        if `obs' >= 20 {
            qui reg TCA_W CFOt_1_W CFOt_W CFOt1_W C_Rev_W PPE_W if Fama_French == `Fama_French' & year == `year', robust
            
            predict double AQ, residuals 
            g res_`obs'=AQ
            drop AQ
            }
    }
}

However, it shows the following error:

. 
. levelsof Fama_French, local(industries)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48

. 
. 
. 
. foreach Fama_French of local industries {
  2. 
.     qui levelsof year if Fama_French == `Fama_French', local(years)
  3. 
.     
. 
.     foreach year of local years {
  4. 
.         qui count if Fama_French == `Fama_French' & year == `year'
  5. 
.         local obs = r(N)
  6. 
. 
. 
.         if `obs' >= 20 {
  7. 
.             qui reg TCA_W CFOt_1_W CFOt_W CFOt1_W C_Rev_W PPE_W if Fama_French == `Fama_French' & year == `year', robust
  8. 
. 
. 
.             predict double AQ, residuals 
  9. 
. g res_`obs'=AQ
 10. 
. drop AQ
 11. 
.             }
 12. 
.     }
 13. 
. }
(51,222 missing values generated)
(51,222 missing values generated)
(51,222 missing values generated)
(51,222 missing values generated)
(51,222 missing values generated)
variable res_29 already defined
r(110);

[Karim Swopon]

Sorry, I forgot to explain in my previous post that I am measuring the accrual quality of firms. The condition is that each industry should have at least 20 observations in a given year. The residual is my desired variable AQ (Accrual Quality) if the criteria are met. Is there any other way to do it?

[Nick Cox]

Each time around your loops you're trying to put residuals in a new variable with name dependent on the sample size. That's not necessary and it's going to fail the first time you see a sample size you've seen before.

You can filter out subsets with less than 20 observations, but that still leaves the possibility that some subsets have missing values on any variable fed to the regression.

A nuance here is that robust standard errors are immaterial if all you want are residuals.

This may get you closer to what you want, but manifestly I can't check with your dataset.

bysort Fama_French year : gen N = _N 
egen group = group(Fama_French year) if N >= 20 
su group, meanonly 
local G = r(max) 

gen residuals = . 

forval g = 1/`G' { 
    qui reg TCA_W CFOt_1_W CFOt_W CFOt1_W C_Rev_W PPE_W if group == `g'        
    if e(N) >= 20 { 
        predict double AQ, residuals 
        replace residuals = AQ if group == `g' 
        drop AQ
    } 
}

[Karim Swopon]

Hi Nick

Thank you very much for your help. I have tried your code and it shows the following error:

 bysort Fama_French year : gen N = _N 

. 
. egen group = group(Fama_French year) if N >= 20 
(1,407 missing values generated)

. 
. su group, meanonly 

. 
. local G = r(max) 

. 
. 
. 
. gen residuals = . 
(108,371 missing values generated)

. 
. 
. 
. forval g = 1/`G' { 
  2. 
.     qui reg TCA_W CFOt_1_W CFOt_W CFOt1_W C_Rev_W PPE_W if group == `g'        
  3. 
.     if e(N) >= 20 { 
  4. 
.         predict double AQ, residuals 
  5. 
.         replace residuals = AQ if group == `g' 
  6. 
.         drop AQ
  7. 
.     } 
  8. 
. }
(51,222 missing values generated)
(21 real changes made)
(51,222 missing values generated)
(22 real changes made)
(51,222 missing values generated)
(20 real changes made)
(51,222 missing values generated)
(23 real changes made)
(51,222 missing values generated)
(20 real changes made)
no observations
r(2000);

I have supplied more data for your convenience to test your code. However, I understand that with that small data set, the condition of more than 20 will not work. if your code works, for example, with >=3, that will do for me. I can change that for my whole data set.

* Example generated by -dataex-. For more info, type help dataex
clear
input str10 cik byte Fama_French int year float(PPE_W CFOt_W CFOt1_W CFOt_1_W TCA_W C_Rev_W) int cid
"0000001750" 42 2016 -75.281555 -2.1165047  -6.242719  -3.116505    -1.961165    -10.194175  1
"0000001750" 42 2018 -2.0669277 -.23994306  .12851548  -.2289071   .016375937    -1.0804557  1
"0000001750" 42 2020 -37.444443  -6.152047 -4.3976607   2.111111     6.122808     25.608187  1
"0000001750" 42 2012 -24.948883  -5.204473  -4.466454  -3.010128    -.3389456    -2.9585304  1
"0000001750" 42 2019   2.535138 -.13387725  .39013535  .24995366     .3371037     .13906917  1
"0000001750" 42 2015   -23.5871 -1.0354838  -.7032258  1.3870968     1.016129    -2.2032259  1
"0000001750" 42 2011  25.682127   3.207224   5.545249  3.6967645     3.083213     10.168536  1
"0000001750" 42 2014  19.026062 -1.1796982   .8806584   3.835391      -3.8107    -12.090535  1
"0000001750" 42 2017      141.6  17.146667  17.973333   5.813333     8.853333     -5.146667  1
"0000001750" 42 2013   2.447334   .4084733 -.12563916  .47596785     .1154127     -.3859752  1
"0000001750" 42 2010   -2.65431  -.4415217  -.3830536  -.6226791   .019210204    -1.7223364  1
"0000001800" 12 2020  -15.02568  -5.967523  -7.955438  -4.634441     .3549849    -2.0422962  2
"0000001800" 12 2021   54.13456   27.79156  25.279684  20.846966    .46701846      22.34037  2
"0000001800" 12 2012  1.5590916   .7671875  .27378368   .7388269     .3937432      .0842314  2
"0000001800" 12 2018   -43.9944 -17.647058 -17.187675  -15.60224    1.5658263     -8.929972  2
"0000001800" 12 2015 -2.1688414 -.51948506  -.5609948  -.6436641     .3511691   -.027673176  2
"0000001800" 12 2017   3.363446  1.2272778   1.388124   .7057398   -.06147406     1.4403437  2
"0000001800" 12 2016 -1.0486771 -.27162483  -.4723541 -.25152645     .0234905    -.03799186  2
"0000001800" 12 2014  305.26315   58.81108   60.55072   59.65454    -38.67117     -51.45112  2
"0000001800" 12 2019  -7.609097 -2.6329114 -3.3902595 -2.7032826   -.27633554    -.56897664  2
"0000001800" 12 2011  -5.178624 -2.5783305  -2.677302  -2.511043     .8482286    -1.0590719  2
"0000001800" 12 2013   15.33969  3.9618595  4.3802147   11.10179   -.22992358    -21.484995  2
"0000001961" 35 2017          0  .13326052  1.0322229   .3719279    .13489898             0  4
"0000001961" 35 2012 -.24175824  11.384616    24.1978   12.57143    1.7142856             0  4
"0000001961" 35 2011  .52380955 -27.238096  -24.66667  -6.809524    16.190477    -19.809525  4
"0000001961" 35 2018          0 -1.8243243 -1.2760618 -.23552124     .6544401             0  4
"0000001961" 35 2016          0  14.967033   5.362638   10.54945    -5.186813             0  4
"0000001961" 35 2020          0  -5.303922  -6.970589  -6.480392     .3627448             0  4
"0000001961" 35 2014          0        -68 -66.217766 -68.180984    -38.67117             0  4
"0000001961" 35 2019          0  -2.443623         -2 -3.4935305     .8724584             0  4
"0000001961" 35 2013          0  -7.339999  -.6333333  -3.453333   -1.9866667             0  4
"0000001961" 35 2015          0  14.328358   20.32836   2.835821    1.8805977             0  4
"0000001961" 35 2021          0 -10.304348  -6.014493   -7.84058   -1.0289855             0  4
"0000002034" 42 2016  -.0832638 -.12798774  -.1791973 -.03354597    -.1030826    -.04653332  5
"0000002034" 42 2014 -1.9002293 -2.2997704  -.7657641 -2.1781552   -1.5053694     -.9627352  5
"0000002034" 42 2017  .15898304   .3287125   .7508897  .23477563     .1505526      .5885359  5
"0000002034" 42 2013 -.25779983  -.3283341  -.3466663 -.18358538   -.09265739     -.7651396  5
"0000002034" 42 2012  -1.471801  -1.098882 -1.9653003 -1.1625673   -.59354043     -2.646791  5
"0000002034" 42 2015  -.7845659  -.3271508 -1.2481766  -.9825112   -1.4983138     -1.441926  5
"0000002034" 42 2011   2.792249  2.2669358  2.1427534  -2.502866     .4744449     10.625272  5
"0000002098" 17 2020  -5.711639  -.7852045  -.7055998 -2.0178425     -1.29042     -2.957178  7
"0000002098" 17 2014 -14.793185 -4.4003787 -1.0222434  -6.128727   -4.1609087    -16.701374  7
"0000002098" 17 2017   10.36855  1.8317152  1.7037884  3.3721685    1.9920044     2.2752714  7
"0000002098" 17 2013  -2.735061 -1.1532638  -.8280346  .22673434     .2605753       -.92742  7
"0000002098" 17 2011 -1.6217674 -.32730445   .2019673    .180073   -.50071394      -1.61082  7
"0000002098" 17 2019 -3.5167906  -1.538027  -.5984936  -.4681452     .4634376     -.5372947  7
"0000002098" 17 2018  -46.87265  -7.036164  -23.11635  -7.564466    -5.536163    -10.646227  7
"0000002098" 17 2015 -3.0228276 -.20291217 -1.6640676  -.8734617   -1.0164396     -.4866134  7
"0000002098" 17 2016 -1.7216733   -.781592  -.4245499 -.09530534   -.12883869    -1.3026828  7
"0000002098" 17 2012  -86.59761  10.143427  -51.59363 -16.438248    -38.67117     -51.45112  7
"0000002098" 17 2021  -4.825659  -.5156987  -.2900993 -.57387906   -1.1099408    -1.8141237  7
"0000002178" 42 2014  4.4662547   .9658501    .522075   .9011568    -.3416019      3.829076  8
"0000002178" 42 2017   65.87167  13.612937  16.178404  3.6223264    -6.296296       47.1314  8
"0000002178" 42 2018  -5.366004  -1.193489 -1.8047794 -1.0042331     .6500424    -16.476295  8
"0000002178" 42 2021  -43.73012 -17.137478  -2.913917   9.306048     9.437182     -51.45112  8
"0000002178" 42 2015   -116.618 -13.933278  -3.797648 -25.776865   -1.2605968       47.1314  8
"0000002178" 42 2012  -13.79343 -3.8133025  -3.077289 -3.9057415    .03988668    -11.655645  8
"0000002178" 42 2020  -5.021983  1.1207652  -2.063936 -1.1946355   -1.5452136     20.093357  8
"0000002178" 42 2019  10.031454  2.7066224 -2.5392585  1.7898717    -.6329824      3.522378  8
"0000002178" 42 2013    3.96079   .8199277   .8787896  1.0160346    .14157064     10.534456  8
"0000002178" 42 2016 -10.641214  -.3875865  -1.456575  -1.422025    -.8170909       47.1314  8
"0000002178" 42 2011  -8.659256 -2.7453825  -2.680406 -1.8163843   -.04225166     -49.30666  8
"0000002186" 37 2012  -177.2381        -68   -63.1746  4.5079365    -9.523816     -51.45112  9
"0000002186" 37 2015  -4.880775  1.8972536  -6.927948 -2.5660744   -1.1121162      .8071082  9
"0000002186" 37 2016  -69.57198        -68  17.789883   22.84825    36.183453     -51.45112  9
"0000002186" 37 2017   5.203324  -1.266482    2.92964   5.939612     3.894737     -6.257064  9
"0000002186" 37 2019   71.32001  -11.04889   19.75111  23.502224     2.560001     -41.24445  9
"0000002186" 37 2018   17.21183   8.454037   -3.97442  -3.654676    -15.69944     15.963228  9
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"0000004828"  2 2012  -58.95237 -1.5087855  -4.409863 -2.1667798   -2.2017667      2.472847 37
"0000004828"  2 2011 -137.67923  -5.221541  -3.635896 -14.912366    -6.142423     -31.71104 37
"0000004904" 32 2021  -30.12902 -1.3239436  -1.823228   -1.32153     -.431741     -.6459565 38
"0000004904" 32 2014  -62.73352  -4.500268  -4.700746  -4.005658     .1834057    -1.6223598 38
"0000004904" 32 2019 -32.931038  -1.755473 -1.5757364 -2.1473002    .26080704     .26076588 38
"0000004904" 32 2020   -24.6486 -1.1091369 -1.1111625 -1.2356508    -.1841279     .18603776 38
"0000004904" 32 2018  -20.61787  -1.473503 -1.2046266 -1.2047112    .09126172    -.21744834 38
"0000004904" 32 2015  -73.38496   -5.40009  -5.064776  -5.169786     .8881542      .6352124 38
"0000004904" 32 2011  -51.93097  -3.533582 -3.5485075  -2.483209     .3992537     -.6427239 38
"0000004904" 32 2017 -33.104305 -2.0965707  -2.564352  -2.218769     .8647176      .4689594 38
"0000004904" 32 2016   -98.3615   -7.16553  -6.770889  -7.639924    -2.321706     .11590296 38
"0000004904" 32 2012  -56.13483 -3.7166586 -4.0117245  -3.701026   -.20127015     .16707377 38
"0000004904" 32 2013  -37.45573  -2.551103 -2.8661075  -2.363467    .13482448     -.2559801 38
"0000005117" 36 2011   .7697681  .13666332  .15264376  -.1133476    -.3687727    -1.0876304 43
"0000005133"  8 2011  -52.98856  -6.855378  -9.574635 -10.576722    -2.709021      -6.03406 44
"0000005133"  8 2010 -287.27307        -68 -66.217766 -68.180984      9.97201      39.08804 44
"0000005337" 39 2014  -40.24112  -1.378985  -3.038756 -2.8467996    .10805554      4.496045 46
"0000005337" 39 2012   59.74518   3.001374   3.897705  4.0793037     .1889291  -.0020825523 46
"0000005337" 39 2013   25.11018  1.5387145   .7453508   1.184866     .4711512     -.2135587 46
"0000005337" 39 2011  -118.7518  -8.467191  -6.229791   -9.40797     .8519064     -37.10141 46
"0000005656" 28 2013   7.505618  -.9887641 -.27715358 -3.3220975    -.4119851      .8576779 48
"0000005656" 28 2014  -50.62295  2.4262295  -11.90164   8.655738     .9508195      5.704918 48
"0000005656" 28 2016  10.925208 -1.7451524 -.59279776  2.0110803     .5872576      .9362881 48
"0000005656" 28 2015  -6.928417 -1.5748373  1.3665943   .3210412     -.208243      .2386117 48
"0000005768" 12 2012   7.520517  3.9097755   4.084076   6.402184     -8.56268    -2.1172042 49
"0000005768" 12 2013   1.705178   .8990235 -.30761155    .860655    -.6989489     .09858707 49
"0000005768" 12 2014  4.5403357 -1.0179625   .7642031  2.9750905    -4.987065     -5.804562 49
"0000005768" 12 2010   8.789649   5.671535   8.714494   7.904347     .5809206      6.231617 49
"0000005768" 12 2011   2.606781  2.3502395  1.4352773  1.5295743     1.566335     -3.456053 49
"0000005981" 14 2016  -2.528854   -.878602 -1.1170623 -1.4875232     .6238024     -.4303647 50
"0000005981" 14 2019 -31.900736 -1.8446298 -17.498383  -2.225797    -7.335361     -2.729573 50
"0000005981" 14 2014   8.993048 -2.3012285   5.302916 -.41941145     4.629725     -5.560678 50
"0000005981" 14 2013  -10.21603   .4830535    2.65042  -3.142413     -3.57416    -1.1529074 50
"0000005981" 14 2017 -4.6360893 -2.0346928  -.3912751 -1.6003448    .16511767    -1.4806104 50
"0000005981" 14 2018  -3.619114 -.29658478  -.2457947  -1.542288     -1.07561    -2.5937445 50
"0000005981" 14 2012  -4.885454  -1.700166  .26135048 -1.6514624    -.3197695     -2.597565 50
"0000005981" 14 2011  -3.504848  -1.292729  -1.330853 -1.0926929   -.13455369     -2.462888 50
"0000005981" 14 2015   20.55378   11.56858   6.832952  -5.020246    -6.759921     -1.362291 50
"0000006176" 20 2012   19.80312   1.668131   2.476496   1.461155    .18258697      -3.40333 52
"0000006176" 20 2014   21.17575    1.32053   1.355568   2.497207    -.5641754    -.54156613 52
"0000006176" 20 2016  305.26315  -38.85517 -66.217766   59.65454    36.183453       47.1314 52
"0000006176" 20 2015  -10.96516  -.6865551  .18863943  -.6688095      .349589     1.1510555 52
"0000006176" 20 2019    19.1229  -.3274128   1.551716  -.3102048    .04867144     -.9931722 52
"0000006176" 20 2017  -157.0116   5.738768   2.436232  2.0413043   -10.432247    -36.425728 52
"0000006176" 20 2018   12.95937  -.2083056  -.2198609   -.490683    -.3739525      -.401772 52
"0000006176" 20 2021  -51.60413   1.851126  3.1744254  -3.924279   -1.2598298    -1.9106288 52
"0000006176" 20 2013 -18.626987  -2.239388 -1.1841949 -1.5084183     .5968105        .70281 52
"0000006176" 20 2011 -287.27307 -28.813187 -32.894634  -55.52812   -15.503555     -23.18035 52
end

[Karim Swopon]

Hi Nick

Just to follow up, the following code is working with my dataset.

bysort Fama_French year: gen N = _N
egen group = group(Fama_French year)
su group, meanonly
local G = r(max)

gen residuals = .

forval g = 1/`G' {
    display "`g'"
    qui count if group == `g'
    if r(N) >= 20 {
        qui reg TCA_W CFOt_1_W CFOt_W CFOt1_W C_Rev_W PPE_W if group == `g'
        predict residuals_temp, residuals
        replace residuals = residuals_temp if group == `g'
        drop residuals_temp
    }
}