I am currently working on district-month level data and interested in examining how the use of police body-worn cameras influences the disposition of citizen complaints against police. I am expecting less dismissed complaints when the body-worn cameras are present during a problematic interaction.

I am working on Chicago data, there are 25 different districts and the data covers 2005-2018. I set the data as time-series at the district-month level. The Chicago police started their full implementation of BWCs in June 2016 with a few districts. However, all districts had BWC at the end of 2017. So, I have time-variant treatment. I've used the following code:

xtreg notsustained BWC##post complaint i.year arrest, fe

notsustained: the number of dismissed complaints at district-month level
BWC (0/1): whether the district had BWC in a given month
Post (0/1): Post is 1 if it's after the first full implementation of BWC in Chicago (Post = 1 if mdate > June2016)
complaint: number of total complaints at the district month level
arrest: number of total arrests at the district-month level

I used traditional difference-in-difference model. However I don't have the same intervention dates for all districts, so I think it may not be appropriate.
Looking forward to your comments.

Best,
Suat