I have a pre-post/treatment-control design (diff-in-diff) with one treatment event. The data is a firm panel data set (5 pre and 5 post firm-years, ~200 firms). The problem - my treatment and control sample show significant differences on multiple covariates (also in the pre-period). Therefore, I want to balance the two samples via entropy matching. Now my question: What is the right way to match the treatment and the control sample here? My suggestion would be to compute entropy weights using the average value (by firm) for each covariates in the pre-period, compute the weights by firm (in the pre-period) and then use the weights also for the post-period. Is that the correct way?
You can use the beginning year of these covariates to obtain the matching weights then merge the weights to your all observations in your panal data. After merging ,you can use the DID methods to get the outcome.