Hi,
I have a regression with fixed effects as below:
Outcome is the binary variable of the company's client being a female or not. The independent variable is the gender of the employee of the company who is assigned to work with the client. For fixed effects, I have an interaction of the office the employee works at, year and d25 which is a binary variable (=1 if age of the client is above 25.)
I have also clustered at the employee level.
Now the reason I calculate the coefficients for all of the triple fixed effects is that I would like to drop the ones with high t-statistic. For example, if 23.office#2002.year#1.d25 in my regression has a t-stat above 10, I would like to drop all of the clients registered at office 23, in year 2002 who are older than 25 years old.
Since I have 190 offices (unique office ids), and 12 years, and a dummy I have a lot of interactions. Too much to actually go through each and drop the significant ones one by one.
How can I do it in a smart quick way?
I hope my question makes sense.
Kind regards,
Neg
I have a regression with fixed effects as below:
Code:
reg female female_employee i.office_id#i.year#i.d25, cluster(employee_id)
I have also clustered at the employee level.
Now the reason I calculate the coefficients for all of the triple fixed effects is that I would like to drop the ones with high t-statistic. For example, if 23.office#2002.year#1.d25 in my regression has a t-stat above 10, I would like to drop all of the clients registered at office 23, in year 2002 who are older than 25 years old.
Since I have 190 offices (unique office ids), and 12 years, and a dummy I have a lot of interactions. Too much to actually go through each and drop the significant ones one by one.
How can I do it in a smart quick way?
I hope my question makes sense.
Kind regards,
Neg
