Hi everybody

I am working with panel data and investigating how employees experience the transfer from a private employer to a public employer (i.e., insourcing) in terms of, e.g., salary. The employees are from 20+ distinct and independent cases of insourcing.
I have identified a date for each case of insourcing (i.e., the transfer of employees from a private company to a public company). Now, I would like to standardize time, so I can investigate the cases jointly.

I have made an example dataset (I have included the desired variable in the example dataset (but it is generated manually).
Code:
clear
input byte id float time byte(treatment treatment1) int time1 byte treatment2 int time2 byte treatment3 int(time3 desired_variable)
1 588 . .    . .    . .    .  985
1 589 . .    . .    . .    .  986
1 590 . .    . .    . .    .  987
1 591 . .    . .    . .    .  988
1 592 . .    . .    . .    .  989
1 593 . .    . .    . .    .  990
1 594 . .    . .    . .    .  991
1 595 . .    . .    . .    .  992
1 596 . .    . .    . .    .  993
1 597 . .    . .    . .    .  994
1 598 . .    . .    . .    .  995
1 599 . .    . .    . .    .  996
1 600 . .    . .    . .    .  997
1 601 . .    . .    . .    .  998
1 602 . .    . .    . .    .  999
1 603 1 1 1000 .    . .    . 1000
1 604 . .    . .    . .    . 1001
1 605 . .    . .    . .    . 1002
1 606 . .    . .    . .    . 1003
1 607 . .    . .    . .    . 1004
1 608 . .    . .    . .    . 1005
1 608 . .    . .    . .    . 1006
1 610 . .    . .    . .    . 1007
1 611 . .    . .    . .    . 1008
1 612 . .    . .    . .    . 1009
1 613 . .    . .    . .    . 1010
1 614 . .    . .    . .    . 1011
1 615 . .    . .    . .    . 1012
1 616 . .    . .    . .    . 1013
1 617 . .    . .    . .    . 1014
1 618 . .    . .    . .    . 1015
1 619 . .    . .    . .    . 1016
1 620 . .    . .    . .    . 1017
1 621 . .    . .    . .    . 1018
1 622 . .    . .    . .    . 1019
1 623 . .    . .    . .    . 1020
2 588 . .    . .    . .    .  980
2 589 . .    . .    . .    .  981
2 590 . .    . .    . .    .  982
2 591 . .    . .    . .    .  983
2 592 . .    . .    . .    .  984
2 593 . .    . .    . .    .  985
2 594 . .    . .    . .    .  986
2 595 . .    . .    . .    .  987
2 596 . .    . .    . .    .  988
2 597 . .    . .    . .    .  989
2 598 . .    . .    . .    .  990
2 599 . .    . .    . .    .  991
2 600 . .    . .    . .    .  992
2 601 . .    . .    . .    .  993
2 602 . .    . .    . .    .  994
2 603 . .    . .    . .    .  995
2 604 . .    . .    . .    .  996
2 605 . .    . .    . .    .  997
2 606 . .    . .    . .    .  998
2 607 . .    . .    . .    .  999
2 608 1 .    . 1 1000 .    . 1000
2 609 . .    . .    . .    . 1001
2 610 . .    . .    . .    . 1002
2 611 . .    . .    . .    . 1003
2 612 . .    . .    . .    . 1004
2 613 . .    . .    . .    . 1005
2 614 . .    . .    . .    . 1006
2 615 . .    . .    . .    . 1007
2 616 . .    . .    . .    . 1008
2 617 . .    . .    . .    . 1009
2 618 . .    . .    . .    . 1010
2 619 . .    . .    . .    . 1011
2 620 . .    . .    . .    . 1012
2 621 . .    . .    . .    . 1013
2 622 . .    . .    . .    . 1014
2 623 . .    . .    . .    . 1015
3 588 . .    . .    . .    .  978
3 589 . .    . .    . .    .  979
3 590 . .    . .    . .    .  980
3 591 . .    . .    . .    .  981
3 592 . .    . .    . .    .  982
3 593 . .    . .    . .    .  983
3 594 . .    . .    . .    .  984
3 595 . .    . .    . .    .  985
3 596 . .    . .    . .    .  986
3 597 . .    . .    . .    .  987
3 598 . .    . .    . .    .  988
3 599 . .    . .    . .    .  989
3 600 . .    . .    . .    .  990
3 601 . .    . .    . .    .  991
3 602 . .    . .    . .    .  992
3 603 . .    . .    . .    .  993
3 604 . .    . .    . .    .  994
3 605 . .    . .    . .    .  995
3 606 . .    . .    . .    .  996
3 607 . .    . .    . .    .  997
3 608 . .    . .    . .    .  998
3 609 . .    . .    . .    .  999
3 610 1 .    . .    . 1 1000 1000
3 611 . .    . .    . .    . 1001
3 612 . .    . .    . .    . 1002
3 613 . .    . .    . .    . 1003
3 614 . .    . .    . .    . 1004
3 615 . .    . .    . .    . 1005
end
format %tm time
As you can see, I have variables (treatment1, treatment2, and treatment3) indicating when the insourcing occurred (i.e., when an employer was transferred). Also, I have variables (time1, time2, and time3) with values of 1000, which identifies when the cases of insourcing occurred in standardized time.

I would like a variable that counts backward and forward from the occurrence of the treatment. This would provide standardized time periods equal across the different cases. My goal is to use this in a regression, such as
Code:
xtreg salary i.treatment##i.time_standardized x1 x2 x3, fe
Thank you
Gustav