From readingTwo-way fixed effect (TWFE) has been used for 2 decades for examining the appendixchange of some particular objectives after an event, or "generalized Difference-in-Differences (DiD)".
However, Goodman, 2018, Imai and Kim, 2020 and Chaisemartin,2020 and other papers documented that it is an appropriate design, especially for staggered DiD (different countries implement the same laws in different time periods). The reason is because of the heterogeneous impact of laws over time.
Asking for solution: I am wondering what is the solution for the TWFE, is it killed from now, so now what should we do if we want to conduct the DiD testing with multiple time periods and groups?
For example, in a note, Bacon,2019, he said says
- “Is DD wrong?” Not in general. The DD research design—comparing outcomes for groups whose treatment status changes to groups whose treatment status does not change—still can be a good idea. The DD specification—estimating the coefficient a single post-treatment dummy—is a bad idea when your treatment effects vary over time (get bigger with time since treatment). In this case, just summarize your findings in a different way—event-study or a linear trend-break, for instance.
The DD specification—estimating the coefficient a single post-treatment dummy—is a bad idea when your treatment effects vary over time (get bigger with time since treatment). In this case, just summarize your findings in a different way—event-study or a linear trend-break, for instance.
The highlighted words are the one I want to focus, can I ask does it mean that if the treatment effect get weaker with time since treatment, so, DD specification is no longer a bad idea? Or,in other words, coefficient in a single post-treatment dummy is a bad idea when my treatment effects vary over time, no matter what getting bigger or weaker since treatment? What does he mean then?