Missing Values In _did_cohort Variable After xthdidregress

[Hwanseong Pak]

Dear all,

I faced a problem where the _did_cohort variable has missing values after running xthdidregress because some variables in that row are missing.
I am wondering if this affects the results and if so, I would like to know if there is a solution to this.
I want all the cells to show either 1. the first year of treatment or 2. Never treated like all other cells.
I appreciate your help in advance.

Kind regards,
Pak

[Carlo Lazzaro]

Pak:
no matter your statistical code, Stata applies listwise deletion.
Therefore, any observation with missing vaues in any variables will be omitted.
The only fix is to take care of missing values before analysing your dataset.

[Hwanseong Pak]

Thank you for the prompt response.
Do you suggest I delete the firms with lacking data? Would this reduce noise or create bias?
My list of firms is already a sample.

[Carlo Lazzaro]

Pak:
I recommend you to deal with missing values first.
Unless data are missing completely at random (unfortunately, it rarely occurs) deleting observations with missing values is a bad methodological habit, as you may end out with a made up sample whose relationship with the original one is tenuous at best.

[Hwanseong Pak]

Carlo Lazzaro
Hello!

My missing variables are MAR and I want to conduct xthdidregress.
However, it is not possible to conduct xthdidregress after treating missing variables.
Do you have any suggestions on how to tackle this?

Kind regards,
Pak

[Carlo Lazzaro]

Pak:
unfortunately, -xthdidregress. is not included in -Estimation — Estimation commands for use with mi estimate-.

[Jeff Wooldridge]

Pak: A few things. First, please show what you type and what Stata reports, within code delimiters. Second, if your data are MAR as a function of either the fixed effects or the X(i,t), then using the complete cases only produces consistent (even unbiased) estimators. There seems to be some confusion on this point. Filling in missing data on controls using observed data works under different assumptions. Because xthdidregress is based on fixed effects, it's actually fairly robust to missing data. I don't know that I'd worry about _did_cohort missing because those observations aren't being used.

[Hwanseong Pak]

Jeff Wooldridge Thank you for the response. To be honest, I am trying everything to make my results make sense. I am changing my control variables, regression model, but nothing seems to work. No results are significant for any interpretation. I am trying to match variables based on industry as a next step. Is it possible to conduct xthdidregress after matching? If yes, how do you do it?

[Jeff Wooldridge]

Without giving us more information about your data set (using -dataex-) and without showing what you typed and the results you got, it's very difficult to give any advice. Please read the FAQ.

[Hwanseong Pak]

Jeff Wooldridge I apologize for the lack of information.
My research is about how the Emission Trading System Affects Corporate Innovation (Patent Count and R&D Expenditure). There are 3 phases of the policy, and I am looking at the effect of the first two phases, as the third phase is still in effect. This is why I am adopting xthdidregress (staggered did).

My variables are:
Dependent Variables: PC (Patent Count), RDE (R&D Expenditure)
Independent Variables: EBITDA, TA (Total Assets), ROE (Return on Equity), DE (Debt to Equity Ratio), RG (Revenue Growth), HHI (Herfindahl–Hirschman index)

Subsequently, my model is as follows:
xthdidregress twfe (PC EBITDA TA ROE DE RG HHI) (treatment), group(ticker)
xthdidregress twfe (RDE EBITDA TA ROE DE RG HHI) (treatment), group(ticker)

Right now, I am thinking of matching companies (ticker) by industry, as my results are not significant for any interpretation. As industry has a very big impact on which firms are regulated by the policy, I thought matching would be appropriate. I have no knowledge on how to conduct xthdidregress after psmatch2. Can you please provide guidance on this?

I attached my results for the command dataex below.

* Example generated by -dataex-. For more info, type help dataex
clear
input long ticker int(year patentcount) double(rdexp revenue ebitda totalassets) byte industry double(industrysize ms ms2 hhi roe der revenuegrowth) byte(treatment phase1 phase2 phase3) float(PC RDE EBITDA TA ROE DE RG HHI _did_cohort)
 20 2012    0     0     210   20.9   304.9 19  49007.80000000001   .004285032178551169 .0000183615007712  .26908771312819163  .574  4.41 -4.77 0 0 0 0    0     0      .       .     .     .     .         .    .
 20 2014    8  10.8   195.6   18.8   286.8 19 45245.299999999996    .00432310096297295 .0000186892019361   .2364559689743737  2.16  1.74 -3.07 0 0 0 0    8  10.8   13.8   299.5  .446  2.64  -1.4 .25796783    0
 20 2021    2  14.6     246     29   375.9 19  46635.69999999999   .005274928863510144 .0000278248745151  .16739994582911943  5.55  3.18   7.7 0 0 0 0    2  14.6     31   398.6  8.92  4.56 -11.4 .14765108    0
 20 2018    9    14   275.4   20.3   332.9 19           45809.26   .006011884933308243 .0000361427604513   .1974434523816729  3.39     .  18.4 0 0 0 0    9    14   20.9   343.8  17.1     .  9.02  .2070064    0
 20 2022    2  15.3   270.7   33.7   366.7 19  56303.19999999999   .004807897242075051 .0000231158758904   .1705451379754811  5.83  1.75  16.2 0 0 0 0    2  15.3     29   375.9  5.55  3.18   7.7 .16739994    0
 20 2016    4  11.4   197.3   19.1   269.6 19            36047.6   .005473318612057391 .0000299572166291    .210061365693561  10.9     .  6.39 0 0 0 0    4  11.4   14.2   269.5  2.44  .869  4.56 .20636405    0
 20 2013    1  11.7   208.6   13.8   299.5 19 48030.700000000004   .004343055587363915 .0000188621318349  .25796781712122063  .446  2.64  -1.4 0 0 0 0    1  11.7   20.9   304.9  .574  4.41 -4.77  .2690877    0
 20 2017    4  13.2   242.3   20.9   343.8 19 45706.700000000004  .0053011921665751405  .000028102638387  .20700641256149546  17.1     .  9.02 0 0 0 0    4  13.2   19.1   269.6  10.9     .  6.39 .21006137    0
 20 2015    6  11.3   189.7   14.2   269.5 19 36312.600000000006   .005224081999085715 .0000272910327332  .20636404584012266  2.44  .869  4.56 0 0 0 0    6  11.3   18.8   286.8  2.16  1.74 -3.07 .23645596    0
 20 2019    0  14.1     266   18.2     326 19 46260.500000000015    .00575004593551734 .0000330630282606  .18155166286229313  3.05  2.31  .179 0 0 0 0    0  14.1   20.3   332.9  3.39     .  18.4 .19744346    0
 20 2020    3  15.5     250     31   398.6 19            39865.1    .00627114945152527 .0000393273154434  .14765106909502607  8.92  4.56 -11.4 0 0 0 0    3  15.5   18.2     326  3.05  2.31  .179 .18155167    0
 40 2012    0     0    92.1  -2.45   112.2  2 201571.49999999994 .00045690983100289483 2.08766593667e-07   .1885935321486032 -12.7  17.1 -23.3 0 0 0 0    0     0      .       .     .     .     .         .    .
 40 2019    0  1.52   114.8  -18.2   124.8  2           205195.7  .0005594659147340807 3.13002109749e-07    .171315662779402 -78.1 202.9 262.6 0 0 0 0    0  1.52  -14.6   139.1 -42.5  67.7 -12.2 .17295003    0
 40 2013    0     0    94.3   .093   110.8  2 201561.09999999998  .0004678482107906734  2.1888194834e-07  .18758976558400361 -11.2  18.5  1.66 0 0 0 0    0     0  -2.45   112.2 -12.7  17.1 -23.3 .18859354    0
 40 2015    0  .095    68.5  -11.1   133.9  2 174772.80000000002 .00039193741817948786 1.53614939769e-07   .1881711454983053 -29.2  90.1  1.15 0 0 0 0    0  .095  -7.97   125.4 -24.7 123.9 -19.9 .19341056    0
 40 2018    0  1.97    32.8  -14.6   139.1  2 212054.49999999994 .00015467721741344799 2.39250415868e-08  .17295003356420358 -42.5  67.7 -12.2 0 0 0 0    0  1.97  -23.6   101.5 -58.4 112.6 -50.9 .17827345    0
 40 2022    0  3.49    93.1  -1.92   118.6  2  280007.1999999999  .0003324914502198516 1.10550564469e-07      .1511141851226 -28.8 134.2 -12.3 0 0 0 0    0  3.49   .487   141.4 -23.8  91.4  13.3 .15308917    0
 40 2014    2  .084    73.1  -7.97   125.4  2           198469.9 .00036831781544707787 1.35658013176e-07  .19341056464598963 -24.7 123.9 -19.9 0 0 0 0    2  .084   .093   110.8 -11.2  18.5  1.66 .18758976    0
 40 2016    0  .084    70.4  -4.15   123.7  2 164445.59999999998  .0004281050998020015 1.83273976476e-07    .183367433757344 -15.3  99.5   5.1 0 0 0 0    0  .084  -11.1   133.9 -29.2  90.1  1.15 .18817115    0
 40 2017    0  1.57      39  -23.6   101.5  2 211521.50000000003  .0001843784201605983 3.39954018209e-08  .17827345446722317 -58.4 112.6 -50.9 0 0 0 0    0  1.57  -4.15   123.7 -15.3  99.5   5.1 .18336743    0
 40 2021    0   2.8   112.1   .487   141.4  2 260667.70000000004  .0004300494460955461 1.84942526087e-07    .153089163972299 -23.8  91.4  13.3 0 0 0 0    0   2.8   4.06   143.2   -27  83.3 -11.1 .14754152    0
 40 2020    0  2.98   108.3   4.06   143.2  2 224050.10000000003 .00048337403107608513 2.33650453919e-07  .14754151827825776   -27  83.3 -11.1 0 0 0 0    0  2.98  -18.2   124.8 -78.1 202.9 262.6 .17131567    0
 80 2012    2     0    1913  271.6  3308.3 50             2714.1     .7048376994215394 .4967961825258483   .5340545624455197  7.31  96.6  48.1 0 0 0 0    2     0      .       .     .     .     .         .    .
 80 2014    5     0  1715.4  198.3  3135.5 50             2576.2     .6658644515177394   .44337546779502   .4989837672590013  1.57  88.4 -1.33 0 0 0 0    5     0  267.4  3365.7  5.67  94.8 -6.74  .5005478 2015
 80 2021    6     0  1849.4    268  3048.3 50             2904.1     .6368238008333047 .4055445533077766  .48912777280912473  6.62  97.4 -2.37 1 0 0 1    6     0  329.2  3030.3  8.22 102.6  10.9 .53378457 2015
 80 2019    0     0  1762.7  203.9  2833.2 50             2654.3     .6640922277059865 .4410184868994999   .5099058522560167 -3.86 113.4  7.93 1 0 1 0    0     0  193.5  3079.4  1.87  91.6 -.227   .491814 2015
 80 2016    4     0  1570.1  201.6  2825.1 50             2412.2      .650899593731863 .4236702811203043  .49228132542201575  2.94  84.4 -.904 1 1 0 0    4     0  215.8  2940.7  4.02  84.7  1.88  .4966504 2015
 80 2013    1     0  1797.4  267.4  3365.7 50             2678.3     .6710973378635702 .4503716368875709   .5005478162060147  5.67  94.8 -6.74 0 0 0 0    1     0  271.6  3308.3  7.31  96.6  48.1  .5340546 2015
 80 2020    7     0  2073.3  329.2  3030.3 50               3023      .685841878928217 .4703790828917871   .5337845401595815  8.22 102.6  10.9 1 0 1 0    7     0  203.9  2833.2 -3.86 113.4  7.93  .5099059 2015
 80 2015   16     0    1621  215.8  2940.7 50             2456.6     .6598550842628023 .4354087322274699   .4966503545927571  4.02  84.7  1.88 1 1 0 0   16     0  198.3  3135.5  1.57  88.4 -1.33  .4989838 2015
 80 2022    5     0    1986  275.4  2650.6 50            3206.48     .6193707741822809 .3836201559111581  .47546370534188376   7.7  87.4  13.4 1 0 0 1    5     0    268  3048.3  6.62  97.4 -2.37  .4891278 2015
 80 2018    6     0  1693.8  193.5  3079.4 50             2616.2     .6474275666997936 .4191624541228157   .4918140317004548  1.87  91.6 -.227 1 0 1 0    6     0  193.7  3283.5  1.02    86 -.017  .4851548 2015
 80 2017    7     0  1769.1  193.7  3283.5 50 2754.3999999999996     .6422814406041244 .4125254489445094  .48515485091042054  1.02    86 -.017 1 1 0 0    7     0  201.6  2825.1  2.94  84.4 -.904  .4922813 2015
100 2012   19  23.3     730   48.9  1319.2 42 3415.2000000000003    .21375029280862026 .0456891876757709  .11852078105783789  7.01  .682  14.3 0 0 0 0   19  23.3      .       .     .     .     .         .    .
100 2013   12  30.3   893.8   76.5  1434.1 42 3568.1000000000004     .2504974636361088 .0627489792881237   .1338031730980072  7.16  4.24  21.5 0 0 0 0   12  30.3   48.9  1319.2  7.01  .682  14.3 .11852078    0
100 2018   16  66.5  1364.3     98  1952.7 42             4298.8      .317367637480227 .1007222173197808   .1574826930566396  3.58  7.18  3.87 0 0 0 0   16  66.5  131.9  1960.8  7.07  8.33  10.7 .15495074    0
100 2015   15  33.2   959.2   99.3  1597.9 42             3465.5     .2767854566440629 .0766101890096624  .13985110148864996  9.55  11.1  10.9 0 0 0 0   15  33.2   92.4  1480.5  7.26   5.5  7.83 .13081728    0
100 2022   10  85.2  1412.1   62.9  1966.2 42             4982.5      .283411941796287  .080322328752742  .15638156286591628  4.59  4.87  5.22 0 0 0 0   10  85.2     85  2068.4  5.24  6.45  4.19  .1522445    0
100 2020   12 131.1  1488.5  132.4  2199.4 42             4681.4     .3179604391848593 .1010988408866286  .15943105335451727  10.9  6.67  9.42 0 0 0 0   12 131.1   61.7  1833.9  2.22  5.54 -2.53  .1510989    0
100 2016   11  43.7  1097.1  116.9  1699.4 42 3577.7999999999993     .3066409525406675 .0940286737750479  .15312211685603377  11.3  13.1    17 0 0 0 0   11  43.7   99.3  1597.9  9.55  11.1  10.9 .13985111    0
100 2021    5  89.6  1416.9     85  2068.4 42  4566.800000000001    .31026101427695535 .0962618969801651   .1522445139100027  5.24  6.45  4.19 0 0 0 0    5  89.6  132.4  2199.4  10.9  6.67  9.42 .15943106    0
100 2017    8  66.9  1368.8  131.9  1960.8 42             4343.5    .31513756187406466 .0993116829039299   .1549507331107909  7.07  8.33  10.7 0 0 0 0    8  66.9  116.9  1699.4  11.3  13.1    17  .1531221    0
100 2019   10  83.9  1282.3   61.7  1833.9 42             4254.6    .30139143515254074 .0908367971833082  .15109890190630856  2.22  5.54 -2.53 0 0 0 0   10  83.9     98  1952.7  3.58  7.18  3.87  .1574827    0
100 2014    8  28.3   932.2   92.4  1480.5 42 3657.8999999999996    .25484567648104106 .0649463188210794  .13081728152134542  7.26   5.5  7.83 0 0 0 0    8  28.3   76.5  1434.1  7.16  4.24  21.5 .13380317    0
210 2012   54     0  9640.5  480.7 10348.6  1            22199.4    .43426849374307414 .1885891246578784  .36680843507965966  8.39  30.6  28.4 0 0 0 0   54     0      .       .     .     .     .         .    .
210 2018   30     0    9867  812.6 11528.2  1 30585.900000000005    .32259962924092467 .1040705207863821    .407226253907012  11.5    44   -11 1 0 1 0   30     0    414 12546.1  9.37  55.4  25.2  .3873867 2015
210 2019   21     0  1438.2  245.2   11646  1 24662.100000000006   .058316201783303115 .0034007793904309   .6234349828759616  3.76  40.3 -84.9 1 0 1 0   21     0  812.6 11528.2  11.5    44   -11  .4072263 2015
210 2021    0     0  1980.2  280.2  6867.9  1            29840.7    .06635903313260078 .0044035212782936    .556986089782448  16.2  67.6  50.6 1 0 0 1    0     0  155.3 12774.3  .423  74.2 -5.68  .6565554 2015
210 2014   38     0  8515.6   -184  9708.4  1            22882.6      .372143025705121 .1384904315809623  .36182302274508055 -9.44  42.7 -5.61 0 0 0 0   38     0   23.1 10214.1 -.211  37.9 -3.96  .3667828 2015
210 2016   48     0  8184.9  338.5 10292.9  1 23812.399999999994    .34372427810720474  .118146379360319  .37947793555326553  5.89    55  3.58 1 1 0 0   48     0  290.2 10252.7  4.68  61.5  2.36   .365754 2015
210 2020    5     0  1439.1  155.3 12774.3  1 27616.700000000004    .05210977415838966 .0027154285628384   .6565553431602359  .423  74.2 -5.68 1 0 1 0    5     0  245.2   11646  3.76  40.3 -84.9   .623435 2015
210 2022    0  52.9  4113.9  514.4  9545.1  1            36317.6    .11327565698173887 .0128313744646446   .4557034013487292  2.18 117.1 119.3 1 0 0 1    0  52.9  280.2  6867.9  16.2  67.6  50.6  .5569861 2015
210 2015   30     0  8084.7  290.2 10252.7  1 22669.600000000002     .3566317888273282   .12718623280218  .36575403429666115  4.68  61.5  2.36 1 1 0 0   30     0   -184  9708.4 -9.44  42.7 -5.61   .361823 2015
210 2017   46     0 11547.3    414 12546.1  1 31061.399999999994    .37175722922984805 .1382034374846538  .38738675005964707  9.37  55.4  25.2 1 1 0 0   46     0  338.5 10292.9  5.89    55  3.58 .37947795 2015
210 2013   55     0  9327.4   23.1 10214.1  1 23020.299999999996    .40518151370746697 .1641720590502742   .3667827710225088 -.211  37.9 -3.96 0 0 0 0   55     0  480.7 10348.6  8.39  30.6  28.4  .3668084 2015
220 2012    0  .729    51.2   3.27       . 42 3415.2000000000003   .014991801358631998 .0002247541079767  .11852078105783789  .378  3.75 -.817 0 0 0 0    0  .729      .       .     .     .     .         .    .
220 2013    0  .887    59.3   5.53    90.3 42 3568.1000000000004   .016619489364087327  .000276207426723   .1338031730980072  4.26  10.9 -.031 0 0 0 0    0  .887   3.27       .  .378  3.75 -.817 .11852078    .
220 2019    1  .759    78.7    9.7   119.3 42             4254.6     .0184976260988107 .0003421621712914  .15109890190630856  6.27  29.8  9.36 0 0 0 0    1  .759    6.7   121.5  5.08  35.2 -.817  .1574827    0
220 2020    0  3.79    90.2   9.35   142.3 42             4681.4    .01926774041953262 .0003712458208745  .15943105335451727  2.32    17  7.97 0 0 0 0    0  3.79    9.7   119.3  6.27  29.8  9.36  .1510989    0
220 2015    2  1.37    56.3   1.63    89.2 42             3465.5    .01624585196941278 .0002639277062121  .13985110148864996   -11  20.9  7.01 0 0 0 0    2  1.37    2.3    86.7  2.23  1.81 -1.17 .13081728    0
220 2017    2  2.03    78.4   6.73   113.7 42             4343.5   .018049959709911363 .0003258010455294   .1549507331107909     .  23.4     . 0 0 0 0    2  2.03   5.26      96  5.94  22.6  7.53  .1531221    0
220 2016    1  1.49    59.2   5.26      96 42 3577.7999999999993   .016546481077757285 .0002737860360566  .15312211685603377  5.94  22.6  7.53 0 0 0 0    1  1.49   1.63    89.2   -11  20.9  7.01 .13985111    0
220 2021    2  3.37    97.1   4.79     162 42  4566.800000000001   .021262152929841457  .000452079147212   .1522445139100027 -.749  30.4  17.9 0 0 0 0    2  3.37   9.35   142.3  2.32    17  7.97 .15943106    0
220 2018    2  1.28    74.6    6.7   121.5 42             4298.8    .01735368009677119 .0003011502129011   .1574826930566396  5.08  35.2 -.817 0 0 0 0    2  1.28   6.73   113.7     .  23.4     . .15495074    0
220 2014    0  .965    56.7    2.3    86.7 42 3657.8999999999996   .015500697121299109 .0002402716112463  .13081728152134542  2.23  1.81 -1.17 0 0 0 0    0  .965   5.53    90.3  4.26  10.9 -.031 .13380317    0
220 2022    0  6.85   110.4   3.53   155.2 42             4982.5    .02215755143000502 .0004909570853733  .15638156286591628 -3.51  31.8  20.1 0 0 0 0    0  6.85   4.79     162 -.749  30.4  17.9  .1522445    0
230 2012   10  20.6   341.1     25   509.3 44            8564.24    .03982840275377617  .001586301665917  .05180046413470875  5.39  28.5  4.11 0 0 0 0   10  20.6      .       .     .     .     .         .    .
230 2016    0  .982    33.9   7.06   169.5 44             7907.3   .004287177671265792  .000018379892385  .04226107907461352  -1.9  59.5  6.45 0 0 0 0    0  .982   11.3   599.6 -1.51  54.7 -90.8 .04340304    0
230 2014    4  20.2   382.5   28.2   568.3 44  8074.940000000001    .04736877301874688 .0022438006573016     .04381695117665  3.62  43.4  5.64 0 0 0 0    4  20.2   32.5   595.1  2.07    51  8.95 .04629913    0
230 2013    5    22   374.4   32.5   595.1 44  8936.439999999999     .0418958780006356 .0017552645934441 .046299134878757905  2.07    51  8.95 0 0 0 0    5    22     25   509.3  5.39  28.5  4.11 .05180046    0
230 2018    0  .222    42.3   2.39   238.5 44  9968.499999999998   .004243366604805137 .0000180061601428  .04194993041342003 -2.26  40.8  14.6 0 0 0 0    0  .222   .632   256.7 -.395  37.9  .734 .04078029    0
230 2022    0    95   537.2  -46.8   685.9 44 12120.070000000003    .04432317635129169 .0019645439618677  .04728683308805445 -93.7 147.7    14 0 0 0 0    0    95    -35     773 -50.2 152.5 -.687 .04687164    0
230 2017    0  .006    38.4   .632   256.7 44 10333.599999999997  .0037160331346287848 .0000138089022577  .04078028821186821 -.395  37.9  .734 0 0 0 0    0  .006   7.06   169.5  -1.9  59.5  6.45 .04226108    0
230 2021    0  92.2   497.3    -35     773 44 11942.199999999997    .04164224347272698 .0017340764414419 .046871635010089535 -50.2 152.5 -.687 0 0 0 0    0  92.2   25.5   745.2 -9.65  75.3 104.1 .04778669    0
230 2019    0  25.3   253.1  -4.23   701.4 44  9971.100000000004   .025383357904343543    .0006443148585  .04366561425079277 -15.2  72.6 521.1 0 0 0 0    0  25.3   2.39   238.5 -2.26  40.8  14.6 .04194993    0
230 2015    1  .194    32.5   11.3   599.6 44            7246.61   .004484855677344303 .0000201139304466  .04340304502232464 -1.51  54.7 -90.8 0 0 0 0    1  .194   28.2   568.3  3.62  43.4  5.64 .04381695    0
230 2020    0  62.9   548.1   25.5   745.2 44 11118.100000000004   .049297991563306665 .0024302919721759  .04778668831672438 -9.65  75.3 104.1 0 0 0 0    0  62.9  -4.23   701.4 -15.2  72.6 521.1 .04366561    0
270 2012  993     0   44419 4031.5 30461.7 13            64012.2     .6939145975298459 .4815174686650079  .48986767242940177  25.5    23  9.38 0 0 0 0  993     0      .       .     .     .     .         .    .
270 2021 2966 878.8 58650.7 6116.6 56121.8 13            81460.1     .7199929781574046 .5183898885959689   .5236967938511252  14.7  27.5  18.1 1 0 0 1 2966 878.8 3939.3 55584.1  5.05  34.9  1.76 .51424724 2015
270 2013  719     0 45086.6 3815.7   34273 13            64691.2     .6969510536208944 .4857407711432748  .49341187219990407  20.6  16.5  .751 0 0 0 0  719     0 4031.5 30461.7  25.5    23  9.38  .4898677 2015
270 2020 3025 823.8   54369 3939.3 55584.1 13            76238.2     .7131464279062203 .5085778276354018   .5142472346444432  5.05  34.9  1.76 1 0 1 0 3025 823.8 3585.3 47938.7   6.5  23.1  7.34  .5105599 2015
270 2019 2967 791.7   50365 3585.3 47938.7 13            70899.4     .7103727252980985 .5046294088474477   .5105598929643038   6.5  23.1  7.34 1 0 1 0 2967 791.7 2777.8 46518.4  4.27  24.5  1.19  .5033517 2015
270 2015  838   651 42083.2 3210.6 39073.8 13 59463.899999999994     .7077100560171802 .5008535233878403   .5075396562979266  11.3  26.1  5.15 1 1 0 0  838   651 3218.6 37603.5    14  20.9 -1.05   .489776 2015
270 2018 4005 776.1 48659.2 2777.8 46518.4 13            69005.5     .7051495895254726 .4972359436079425   .5033516829397732  4.27  24.5  1.19 1 0 1 0 4005 776.1 2357.1 48952.9  3.62  32.6  1.56  .5093842 2015
270 2016  962 658.2 43785.5 3446.3 42270.7 13            61373.3     .7134291296052192 .5089811229692607    .515279420095674  10.8  30.4  6.45 1 1 0 0  962 658.2 3210.6 39073.8  11.3  26.1  5.15 .50753963 2015
270 2017 4407 723.2 50114.9 2357.1 48952.9 13            70651.8     .7093223385674533 .5031381799908009   .5093842169737886  3.62  32.6  1.56 1 1 0 0 4407 723.2 3446.3 42270.7  10.8  30.4  6.45  .5152794 2015
270 2014  838     0 43148.9 3218.6 37603.5 13            62127.4     .6945228675270493 .4823620135179952   .4897759803632214    14  20.9 -1.05 0 0 0 0  838     0 3815.7   34273  20.6  16.5  .751  .4934119 2015
270 2022 1273 999.7 68828.2   7678 58611.9 13            95010.7     .7244257752021614 .5247927037772525   .5300685507278473  14.6  19.7  23.9 1 0 0 1 1273 999.7 6116.6 56121.8  14.7  27.5  18.1  .5236968 2015
300 2012   21  .333   356.7  -6.69     573 14             1679.9    .21233406750401806 .0450857562228009  .32090662252554647 -18.9  73.7  13.3 0 0 0 0   21  .333      .       .     .     .     .         .    .
300 2014    0  .848   459.7   13.3   647.4 14             2468.5    .18622645331172777 .0346802919130651   .3533147990792964  -3.6  73.5  18.4 0 0 0 0    0  .848   20.3   605.5  6.98  47.1  11.7  .3151612    0
300 2015    6   .89   421.3   14.2   666.6 14               2267    .18584031760035288    .0345366236458   .3262380340938211 -.162  81.6 -1.19 0 0 0 0    6   .89   13.3   647.4  -3.6  73.5  18.4  .3533148    0
300 2016    2  .805   360.6   21.3   780.7 14             1990.4    .18116961414790997 .0328224290905026  .33317408993231823   7.7  66.4 -12.4 0 0 0 0    2  .805   14.2   666.6 -.162  81.6 -1.19   .326238    0
300 2022    0  .488    39.9   6.87   431.9 14 1701.2000000000003   .023454032447683983  .000550091638057   .5139485155607783 -32.1 222.5 -89.2 0 0 0 0    0  .488   16.6     499 -1.62   179  2.56  .3686302    0
300 2018    4  .459   452.2   44.8   899.1 14               2407     .1878687162442875 .0352946545432766   .3348272185629471    16  66.7  9.98 0 0 0 0    4  .459   20.9   885.5 -3.18  56.1  5.44 .33777025    0
300 2021    4  1.93     390   16.6     499 14             2875.6    .13562386980108498 .0183938340598217  .36863016810140664 -1.62   179  2.56 0 0 0 0    4  1.93   27.7   434.7 -4.33 143.3 -.948  .3692771    0
300 2013    7  .756   401.3   20.3   605.5 14             1744.7    .23001089012437667 .0529050095758081  .31516118764179407  6.98  47.1  11.7 0 0 0 0    7  .756  -6.69     573 -18.9  73.7  13.3  .3209066    0
300 2019    2  .883   396.1     28   943.4 14               2403    .16483562213899294 .0271707823259489   .3490069615782326  7.11  86.4 -9.17 0 0 0 0    2  .883   44.8   899.1    16  66.7  9.98  .3348272    0
300 2017    4  .651   428.5   20.9   885.5 14             2461.3    .17409499045220003 .0303090657005516   .3377702492983288 -3.18  56.1  5.44 0 0 0 0    4  .651   21.3   780.7   7.7  66.4 -12.4  .3331741    0
300 2020    0  2.17   416.2   27.7   434.7 14 2721.8999999999996    .15290789522025058 .0233808244206871   .3692770746467091 -4.33 143.3 -.948 0 0 0 0    0  2.17     28   943.4  7.11  86.4 -9.17 .34900695    0
390 2012    3     0   408.9   27.2   399.3 32             1003.3     .4075550682746935 .1661011336763901  .29064443163335935  7.42  46.6  4.94 0 0 0 0    3     0      .       .     .     .     .         .    .
end
label values _did_cohort _cohort
label def _cohort 0 "Never treated", modify

[Jeff Wooldridge]

But you haven't shown us the output from the estimation. Maybe you can get more precise estimates by aggregating the estimates with "estat aggregation, dynamic" or "estat aggregation." But you need to show the output before I can say more.