Two-way fixed effect (TWFE) has been used for 2 decades for examining the change of some particular objectives after an event, or "generalized Difference-in-Differences (DiD)".

However, [Goodman, 2018][1], [Imai and Kim, 2020][2] and [Chaisemartin,2020][3] 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][4] says

>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.

What does he mean then?


  [1]: http://goodman-bacon.com/pdfs/ddtiming.pdf
  [2]: https://www.cambridge.org/core/journals/political-analysis/article/on-the-use-of-twoway-fixed-effects-regression-models-for-causal-inference-with-panel-data/F10006D0210407C5F9C7CAC1EEE3EF0D
  [3]: https://www.aeaweb.org/articles?id=10.1257/aer.20181169
  [4]: https://cdn.vanderbilt.edu/vu-my/wp-content/uploads/sites/2318/2019/10/09023516/so_youve_been_told_dd_10_9_2019.pdf