I have an individual-level dataset where each row corresponds to a birth, with information on exact date and dummy variable for C-section. I created dummy variables to indicate whether the birth ocurred around specific types of calendar dates. For a given type, I have 7 dummy variables (indicating date falling in the respective criteria for this type, 1 day before, 2 days before, 3 days before, 1 day after, 2 days after, 3 days after). I did so for 2 types of caldendar dates (type A and type B). These is no collinearity between the types. In a event-study analysis, I regress dummy for C-section (variable d_pc) on these dummy variables (dt_type#_pre*, dt_type#, dt_type#_pos*, where #=A,B, and *=1,2,3).
Bellow is a (simplified) sample of my dataset and the (simplified) regression I would like to run.

Code:
clear
input int dt byte(d_pc dt_typeA dt_typeB dt_typeA_pre1 dt_typeA_pre2 dt_typeA_pre3 dt_typeA_pos1 dt_typeA_pos2 dt_typeA_pos3 dt_typeB_pre1 dt_typeB_pre2 dt_typeB_pre3 dt_typeB_pos1 dt_typeB_pos2 dt_typeB_pos3 pick)
16815 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
16819 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
16834 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
16835 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
16836 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
16851 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
16853 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
16861 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1
16863 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1
16866 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
16868 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
16877 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
16898 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
16911 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1
16913 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
16918 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1
16919 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1
16924 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1
16941 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
16944 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
16960 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
16961 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
16968 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
16979 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
16992 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
17013 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
17034 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
17044 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
17049 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
17053 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
17065 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
17086 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
17090 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
17091 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
17097 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
17108 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
17120 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
17127 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
17139 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
17146 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
17147 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
17151 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
17162 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1
17181 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
17182 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
17185 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
17195 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
17200 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
17201 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
17203 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
17212 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1
17219 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1
17225 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
17226 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
17227 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
17230 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
17234 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
17242 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
17242 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
17245 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
17247 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
17248 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
17259 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1
17269 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
17270 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
17279 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
17284 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1
17285 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1
17295 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
17307 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
17307 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
17314 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
17316 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
17322 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
17336 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
17340 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
17348 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
17354 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
17357 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
17360 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
17381 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
17390 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
17411 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
17411 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
17431 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
17467 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
17472 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1
17475 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1
17476 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1
17477 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1
17477 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1
17491 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
17500 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
17507 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
17511 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
17514 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
17516 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
17521 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
17525 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1
17526 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1
end
format %td dt

reg d_pc dt_typeA - dt_typeB_pos3
I would then like to plot a graph with the coefficients values (and CI) in the y-axis and time in the x-axis (-3days, -2days, -1day, 0, +1day, +2days, +3days). Basically, I would like to have both coefficients plotted for each level of x (relative date to each specific type). For instance, for -3d in the x-axis I would like to have both coefficients dt_typeA_pre3 and dt_typeB_pre3 plotted in the vertical axis.

I only managed to do this separately for each type with -coefplot-. I think I am missing something as the code below doesn't seem to be the most logical way to go.

Code:
local var_list dt_typeA_pre3 dt_typeA_pre2 dt_typeA_pre1 dt_typeA dt_typeA_pos1 dt_typeA_pos2 dt_typeA_pos3
coefplot, vertical drop(_cons) keep(`var_list') order(`var_list') coeflabels(dt_typeA_pre3="-3d" dt_typeA_pre2="-2d" dt_typeA_pre1="-1d" dt_typeA="0" dt_typeA_pos1="+1d" dt_typeA_pos2="+2d" dt_typeA_pos3="+3d")
Many thanks in advance!