Thread by @borusyak

On the 1st anniversary of my twitter account, I'm happy to share w/the #EconTwitter community our #stata commands for event studies:

did_imputation: robust and efficient imputation estimator

event_plot: event study plots after various estimation methods

github.com/borusyak/did_i…

I’ve discussed did_imputation in the 🧵 on our paper w/@XJaravel and @jannspiess

Key points: the estimator is
- robust to heterogeneous effects
- efficient under homoskedasticity
- transparent
- robust to pre-testing
- works w/controls, triple-diffs, etc

https://twitter.com/borusyak/status/1394680063384248320

Now on the plotting command:

With event_plot, you can make event study plots for at least 5 methods:
- our did_imputation
- robust estimators of @CdeChaisemartin-D’Haultfoeuille, Callaway-@pedrohcgs, Sun-Abraham
- traditional OLS

And combine them with each other if you wish!

On this figure you can see:

- how precise each event study estimator is (which of course depends on the DGP)
- how long estimation takes
- how dynamic OLS is biased with heterogeneous effects (Sun-Abraham’s result)

This is likely the first time all estimators are on one graph. The code is on github for you to use in any application!

But don’t overlook the differences in assumptions, properties & interpretation of coefs (e.g. which reference groups are used for causal effects & pre-trends)

Comments and feedback on both commands and on our paper with @XJaravel and @jannspiess are most welcome!

And special thanks to @kylefbutts for the help with preparing the helpfiles for both commands