csdid- parallel trends assumption

[Sharma Swati]

Hello

I am using csdid command and I have the following result:

estat event
ATT by Periods Before and After treatment
Event Study:Dynamic effects
------------------------------------------------------------------------------
             | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
     Pre_avg |  -.1811798   .2795749    -0.65   0.517    -.7291366     .366777
    Post_avg |  -2.414957   1.831923    -1.32   0.187    -6.005459    1.175545
        Tm30 |  -13.20358   .7215255   -18.30   0.000    -14.61774   -11.78941
        Tm29 |   2.027895   .5853005     3.46   0.001     .8807268    3.175063
        Tm28 |  -.6848422   .4508038    -1.52   0.129    -1.568401     .198717
        Tm27 |   15.01779   .5221891    28.76   0.000     13.99432    16.04126
        Tm26 |   26.49495   .6299964    42.06   0.000     25.26018    27.72972
        Tm25 |  -37.29432   1.410723   -26.44   0.000    -40.05928   -34.52935
        Tm24 |   1.270667   2.861961     0.44   0.657    -4.338674    6.880008
        Tm23 |   6.193544   9.482346     0.65   0.514    -12.39151     24.7786
        Tm22 |   1.311684   2.404443     0.55   0.585    -3.400938    6.024306
        Tm21 |   .9185266    2.83065     0.32   0.746    -4.629446    6.466499
        Tm20 |   .8361952   2.821925     0.30   0.767    -4.694676    6.367066
        Tm19 |  -2.905719   5.002753    -0.58   0.561    -12.71093    6.899497
        Tm18 |   5.849199   5.512585     1.06   0.289     -4.95527    16.65367
        Tm17 |  -8.518564   4.736195    -1.80   0.072    -17.80134    .7642075
        Tm16 |  -2.840491   1.979874    -1.43   0.151    -6.720973     1.03999
        Tm15 |   2.777298   2.570612     1.08   0.280    -2.261009    7.815606
        Tm14 |   1.517417   1.204803     1.26   0.208    -.8439539    3.878788
        Tm13 |   -2.15898   2.005195    -1.08   0.282     -6.08909     1.77113
        Tm12 |   .4217594   .8692629     0.49   0.628    -1.281965    2.125483
        Tm11 |   .3339721    1.22641     0.27   0.785    -2.069748    2.737692
        Tm10 |  -1.527944   1.257516    -1.22   0.224     -3.99263    .9367417
         Tm9 |  -1.645971   .9585395    -1.72   0.086    -3.524674     .232732
         Tm8 |    2.60345   1.563854     1.66   0.096    -.4616472    5.668548
         Tm7 |   2.348679   3.353953     0.70   0.484    -4.224948    8.922306
         Tm6 |  -1.317924   4.383561    -0.30   0.764    -9.909546    7.273697
         Tm5 |  -2.789181   2.850594    -0.98   0.328    -8.376243     2.79788
         Tm4 |   1.294631   .8566186     1.51   0.131    -.3843102    2.973573
         Tm3 |   -1.36024   1.085474    -1.25   0.210     -3.48773    .7672505
         Tm2 |  -.8331812   .8735975    -0.95   0.340    -2.545401    .8790384
         Tm1 |   .4278832   .8881955     0.48   0.630    -1.312948    2.168714
         Tp0 |  -.2550995   .6306963    -0.40   0.686    -1.491241    .9810426
         Tp1 |   -2.25114   .9687242    -2.32   0.020    -4.149805    -.352476
         Tp2 |  -2.882293   1.047642    -2.75   0.006    -4.935634   -.8289516
         Tp3 |  -3.322708   1.324161    -2.51   0.012    -5.918015   -.7274002
         Tp4 |  -4.080813   1.404024    -2.91   0.004     -6.83265   -1.328976
         Tp5 |  -3.225497   1.293121    -2.49   0.013    -5.759967   -.6910273
         Tp6 |  -2.740825   1.360054    -2.02   0.044    -5.406481   -.0751679
         Tp7 |  -2.764129   1.479424    -1.87   0.062    -5.663746    .1354889
         Tp8 |  -1.956942   1.507687    -1.30   0.194    -4.911954    .9980706
         Tp9 |  -2.317614   1.435692    -1.61   0.106    -5.131519    .4962907
        Tp10 |  -2.634462   1.421531    -1.85   0.064    -5.420612    .1516879
        Tp11 |  -2.839077   1.569458    -1.81   0.070    -5.915159    .2370045
        Tp12 |  -2.739102   1.594797    -1.72   0.086    -5.864847    .3866425
        Tp13 |  -3.230725   1.622374    -1.99   0.046    -6.410519   -.0509297
        Tp14 |  -3.080694   1.601654    -1.92   0.054    -6.219879    .0584913
        Tp15 |  -3.093499   1.524025    -2.03   0.042    -6.080533   -.1064638
        Tp16 |  -2.742223    1.48909    -1.84   0.066    -5.660785     .176339
        Tp17 |  -2.626583   1.538502    -1.71   0.088     -5.64199    .3888253
        Tp18 |  -2.925291    1.73939    -1.68   0.093    -6.334432    .4838498
        Tp19 |  -2.703404   1.735855    -1.56   0.119    -6.105617    .6988093
        Tp20 |  -2.555481   1.857685    -1.38   0.169    -6.196476    1.085514
        Tp21 |  -2.508957   2.431919    -1.03   0.302    -7.275431    2.257517
        Tp22 |  -3.060866   2.896795    -1.06   0.291    -8.738479    2.616747
        Tp23 |  -.7688686   2.565826    -0.30   0.764    -5.797796    4.260059
        Tp24 |  -.5830077   2.884242    -0.20   0.840    -6.236018    5.070003
        Tp25 |  -.4211932   3.315897    -0.13   0.899    -6.920231    6.077845
        Tp26 |  -.8236493   3.696207    -0.22   0.824    -8.068082    6.420783
        Tp27 |  -2.467305   3.878023    -0.64   0.525    -10.06809    5.133481
        Tp28 |  -2.451432   3.905603    -0.63   0.530    -10.10627     5.20341
        Tp29 |  -4.396237   4.109217    -1.07   0.285    -12.45015     3.65768
        Tp30 |  -4.934456   5.414592    -0.91   0.362    -15.54686    5.677948
        Tp31 |   2.104948   1.060648     1.98   0.047      .026116    4.183779
------------------------------------------------------------------------------

.

Looking at this, I am trying to determine if my parallel trends assumption in the pre-treatment period is satisfied. Can someone help me out, please? If it is satisfied, what are those periods?
Whenever I have to look at similar plots, what am I looking at really to see if this assumption is satisfied?

Thank you!

[FernandoRios]

two things here
1 csdid_plot. Look at the "pre-treatment" effects. they should all cover zero and around zero for PTA to hold
2. In this table look at all the `tmX` values. Same , they should be close to zero, with CI that covers zero
F

[Sharma Swati]

FernandoRios great! Thanks a lot!