So I am having a multicollinearity issue with my current regression. I have a panel data of GP practice QOF scores from 2012-2015 and am using ,fe to calculate practice fixed effects. I have included a dummy (HIGH_2013) to identify which practices scored highly in one category of the QOF scores in 2013. I.e if a practice scored highly in the category in 2013, HIGH_2013 will be equal to 1 for every period for that practice.
I include that variable in my regression, along with a dummy equal to 1 if the year is 2014 and an interaction between the two (it's a difference-in-differences). However if I run the regression HIGH_2013 will be omitted. I believe this is occurring as it is simply acting as an identifier for a practice, which is essentially what fe is doing. Is this correct? Are diff-in-diff and fe fundamentally mutually exclusive or is there some way I can get around this problem?
I include that variable in my regression, along with a dummy equal to 1 if the year is 2014 and an interaction between the two (it's a difference-in-differences). However if I run the regression HIGH_2013 will be omitted. I believe this is occurring as it is simply acting as an identifier for a practice, which is essentially what fe is doing. Is this correct? Are diff-in-diff and fe fundamentally mutually exclusive or is there some way I can get around this problem?