Diff in Diffs
Opened this issue · 6 comments
I coded the classic Diff in Diffs (two way Fixed Effect) outcome model, which is a special case of Synthetic Controls.
I just discovered @junyuan-chen's new package DiffinDiffs.jl which has a few of the latest DiD techniques.
Maybe, if there is interest, at some point there can be an opportunity to join forces?
Maybe create an umbrella organization? (ProgramEvaluation.jl?)
It may be worth comparing SCM w/ the new generation of Diff-in-Diffs estimators.
@vincentarelbundock compares TWFE/Callaway & Sant'Anna/IFE/Matrix Completion in a blog post.
The author has a disclaimer that he's not comparing the performance of alternative estimation strategies ...
That would be interesting. The disclaimer in myblog post is because I don't actually think that the factor structure in the DGP satisfies the Callaway & Sant'Anna assumptions. Baker et al (2021) has a different simulation where C&SA works better:
https://andrewcbaker.netlify.app/
Here's a stripped down version of that simulation that I coded up data.table, but the full code is on Baker's website:
Reminder for self: here is a link to @andrewchbaker's DiD codes for "How Much Should We Trust Staggered Difference-in-Differences Estimates?"
Also @borusyak's https://github.com/borusyak/did_imputation
compares 5 event study estimators in a simulated panel:
- de Chaisemartin-D'Haultfoeuille (https://github.com/shuo-zhang-ucsb/did_multiplegt)
- @bcallaway11 & @pedrohcgs (https://github.com/bcallaway11/did)
- @lsun20 & Abraham (https://github.com/lsun20/EventStudyInteract)
- @borusyak, Jaravel, Spiess
- OLS
Also:
@kylebutts has did2s (R/STATA)
w/:
- TWFE
- @borusyak, Jaravel, Spiess 2021
- @bcallaway11 & @pedrohcgs 2020 (https://github.com/bcallaway11/did)
- Gardner 2021 (did2s package)
- Roth & @pedrohcgs 2021
- @lsun20 & Abraham 2020 (https://github.com/lsun20/EventStudyInteract)
- @asjadnaqvi Diff-in-Diff Notes: STATA/R packages
- @taylorjwright DiD Reading group & Repo