Event Study: best commands and how to do

[Collin James]

I am trying to work on creating event study plots and I am curious what the best command to do this is, and any advice on how to do this. Currently, I am trying with eventdd but haven't gotten too far. I have a couple important variables:
activemonth: This is in stata time like 2020m3, 2020m4,......
callspermonth: This variable counts the number of child abuse welfare calls per month.

My baseline value would be 2020m3, I think, as I am trying to estimate the effect of COVID-19 on the number of calls. I want to compare the drop in calls after COVID with the drop in similar months of the previous years. Can anyone help me think this through? Sorry, not a ton of information--thanks in advance for the help on this as I am a bit confused.

[Collin James]

Can someone help me think about this some more? When I am running a two-way fixed effects model, it looks like this: Y=B*Treat + monthfe + yearfe +error. Basically, I am treating the months of April and May (right after March) of every year except 2020 as control months, while the treated months are those right after COVID actually happened. I am trying to figure out how to plot this type of thing in an event study way that basically shows the coefficient on the years before (May and April of 2019, of 2018, so on...)

I tried making my time variable, activemonth_final, to look different. For instance, I made it =0 if March of 2020, and -1 if March of 2019, and -2 if March of 2018, and so on...then tried running:

eventdd callspermonth, timevar(timevar) fe

But it doesn't seem quite right. Can you help with this? Thanks!

[Fei Wang]

Using -dataex- to display an example data would lead to more specific advice. For now, I assume your data are time series (you may have a panel). Below is an example.

* Example generated by -dataex-. For more info, type help dataex
clear
input float(activemonth callspermonth)
636 104
637 121
638  50
639  55
640  69
641  34
642 103
643 111
644 123
645  79
646  78
647  22
648  63
649  68
650  28
651  68
652  98
653  58
654 123
655  54
656  22
657  14
658 128
659 142
660  71
661  83
662 129
663  89
664  56
665  99
666 112
667 125
668  83
669  91
670  66
671  69
672  68
673  62
674  55
675  71
676  66
677  40
678 112
679 107
680  52
681  23
682 106
683 109
684  61
685 107
686 101
687 125
688  53
689 129
690  68
691 149
692  80
693 152
694  58
695  74
696 114
697 107
698 139
699  58
700  40
701 117
702 130
703 103
704 114
705 103
706  70
707 123
708  82
709  49
710  88
711  71
712 120
713 110
714  53
715  63
716 136
717  74
718  22
719  98
720  19
721  58
722  66
723 242
724 316
725 343
726 280
727 381
728 476
729 504
730 295
731 288
end
format %tm activemonth

gen year = year(dofm(activemonth))
gen month = month(dofm(activemonth))

gen d = year == 2020
gen p = month >= 4

reghdfe callspermonth c.d##ib(3).month, a(year month)

mat result = (r(table)["b", "1.month#c.d".."12.month#c.d"] \ ///
              r(table)["ll".."ul", "1.month#c.d".."12.month#c.d"])'
svmat result, n(col)
gen m = _n in 1/12

twoway connected b m || rcap ul ll m, yline(0, lp(dash)) xlab(1/12) ///
       yti("Effects of COVID-19 on calls") xti("Months") leg(off)

Results in red are effects before Apr and those in blue are effects after Apr. March is treated as the base month.

   month#c.d |
          1  |  -43.14286   51.52471    -0.84   0.405    -146.0153    59.72955
          2  |         -9   51.52471    -0.17   0.862    -111.8724    93.87241
          4  |   183.5714   51.52471     3.56   0.001     80.69902    286.4438
          5  |   262.5714   51.52471     5.10   0.000      159.699    365.4438
          6  |   277.4286   51.52471     5.38   0.000     174.5562     380.301
          7  |   198.1429   51.52471     3.85   0.000     95.27045    301.0153
          8  |   297.5714   51.52471     5.78   0.000      194.699    400.4438
          9  |   407.1429   51.52471     7.90   0.000     304.2704    510.0153
         10  |   445.7143   51.52471     8.65   0.000     342.8419    548.5867
         11  |   237.8571   51.52471     4.62   0.000     134.9847    340.7296
         12  |   215.2857   51.52471     4.18   0.000     112.4133    318.1581

The results above can be plotted as

[Jared Greathouse]

Hey Collin. Check this link, this one, or this one. Happy event studying, plenty of advancements on this in the last year and a half. Collin James

[Jared Greathouse]

Hey Collin. Check this link, this one, or this one. Happy event studying, plenty of advancements on this in the last year and a half. Collin James