Hi all,
I am currently thinking to use difference-in-difference model in my project but not sure whether this is a feasible way I can pursue for my dataset. My project tries to explore the effect of identity shift for an emerging mobile App on its user base growth. My dataset is restricted between 2014 and 2018. Since my focus is on emerging mobile Apps, my dataset will include all new apps launched during this period and trace their monthly user base change. As those Apps launched in different months and the treatment timing (identity shift chosen by an App owner) happened in multiple time, my panel dataset is unbalanced for both control group and treatment group in the sense that I don't have equal number of observation for different entities (Apps). Is this a serious issue when I try to use DID model in this case?
Thanks for taking time to read my question!
Best wishes,
Eric