Dear reader,

Thank you for reading this distress post.
I have a panel dataset with store locations as group variable, and the date as a time variable.

It has 3 explanatory variables:
i.dow (day of week)
discountday (A boolean variable that shows if that specific store runs a discount for the product on that specific day. note that a discount can only be on a Friday)
i.anticipation ( variable that shows how many days before/after the discount day is, only for the store that has the discount. It can take the values -4 until 4, where the Thursday prior to the sale is -1, the Wednesday is -2 etc. remaining observation values are blank.) (this is the objective of my study, so i have to include it)

This obviously causes a lot of collinearity, since for example discountday can only occur on i.dow ==6, and i.anticipation ==-1 can only coincide with i.dow==4.
However, I do need all these regressors to have a good model.
I made a control group by dropping all values of discountday==1, and all values of i.anticipation that are not blank.
Then used:
Code:
xtreg y i.dow, fe
to calculate control values for i.dow.
Is it possible to 'lock' these values, add back the dropped observations,
and then use the other two independent variables AFTER this first command, while retaining the i.dow values from xtreg command?
I have no idea how to do something like this, every help is much appreciated.
Also, if there is a better way to achieve this, I would be interested in hearing that too.
Kind regards, a long term lurker but now new member

Theo