Dear community,

In the context of a difference-in-differences analysis, I would like to perform an events study to check for anticipatory and adjustment effects. My ultimate goal is to create a graph such as the one one page 26 of the following paper

I already checked on Stackoverflow and was suggested to use time series operators.

The simplified version of my panel data set contains a time variable "year", and identifier variable "ID", the dummy variable of interest "presence", and a set of various controls

Lead variables
Code:
gen presence_F1= F1.presence
gen presence_F2= F2.presence
gen presence_F3= F3.presence
Lag variables
Code:
gen presence_L1= L1.presence
gen presence_L2= L2.presence
gen presence_L3= L3.presence
If my preferred specification is:
Code:
reghdfe dependent_variable presence set_of_controls, absorb(year ID)
Would I then need to re-run the preferred specification by simply substituting the variable presence by the respective lead or lag variable? In other words to obtain the coefficients for the graph from above, would the code simply be the following?

Code:
reghdfe dependent_variable presence_F3 set_of_controls, absorb(year ID)
reghdfe dependent_variable presence_F2 set_of_controls, absorb(year ID)
reghdfe dependent_variable presence_F1 set_of_controls, absorb(year ID)
reghdfe dependent_variable presence set_of_controls, absorb(year ID)
reghdfe dependent_variable presence_L1 set_of_controls, absorb(year ID)
reghdfe dependent_variable presence_L2 set_of_controls, absorb(year ID)
reghdfe dependent_variable presence_L3 set_of_controls, absorb(year ID)
Many thanks! I appreciate your guidance!