Pedro H.C. Sant'Anna "Difference-in-Differences with Multiple Time Periods"
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- Опубликовано: 22 авг 2024
- DiD Reading Group Meeting #3 from May 14, 2021 (taylorjwright.....
Pedro H.C. Sant'Anna presents his paper "Difference-in-Differences with Multiple Time Periods" (with Brantly Callaway) (pedrohcgs.gith....
Slides can be found here: pedrohcgs.gith...
Example code using did in R:
This was such a helpful video; Pedro is so clear!
Two quick questions:
1. If I am filtering my sample population to better capture the target demographic of the policy that I am evaluating, is this considered conditioning the parallel trends assumption on a set of pre-treatment covariates?
2. I am a bit confused about post-treatment parallel trends. If one of my treatment groups is negatively impacted by the policy, whereas the rest are positively impacted, is this a volition of this assumption?
Extremely useful! It is such a pleasure to enjoy those viedos. Thanks a lot for the work.
Thank you for this helpful video!
Thank you for uploading this great video!
Thanks for watching, glad you found it useful!
Thank you for this. However, I must appoint that the estimator does fail when we have a common year for the treatment intervention for all treated units. Secondly, the explanation of how to define gvar is really poor in both the code and the video
Hello, thank you for the video.
I've been trying to replicate this methodology but I'm having issues with defining the gvar and get an error message when I run the code. I currently have a dummy for that variable, indicating when a treatment was implemented. Instead of having a value of 1 or 0 for when the treatment was implemented, how should my variable be coded in my dataset? I would really appreciate any advice! Thank you so much!
Hi Petra, hope you solved your issue. If not, the group variable is an id for assign each entry to a group, it isn't a dummy. For example: Say i have 100 entries, each entry is a individual. The first 50 represents the group that's treated in 2004. The last 50 represents the group that's treated in 2005. In this case, we would have two groups: group 1, all the entries that's treated in 2004 and group 2, all the entries that's treated in 2005.
Hope it could help you or anyone with the same issue.
How does he have ATT on g-1 time period when g-1 is the base period that he compares to?
Thanks for this presentation - it complements well your paper. I have one question, does the 'not yet treated' group include cases that are 'never treated'? Also, a slight typo on slide 20, missing a '['.
AS far as I can tell you should dived them up and yes they can be part of the group
This is extremely useful. Do you have the link for the example code using did in R?
It's been a little while since I looked at it, but I believe the code I had used can be found here: github.com/taylorjwright/did_reading_group/blob/main/callaway_santanna/callaway_santanna_castle.R
You can also see some examples from Pedro and Brant in their vignette: bcallaway11.github.io/did/articles/did-basics.html#other-features-of-the-did-package
@@taylorwright3880 Thank you so much! your answer was so fast! :)
@@taylorwright3880 Hi, the code doesn't work at line 76-80. The error is Error in pre_process_did(yname = yname, tname = tname, idname = idname, :
data[, tname] must be numeric
@@JasmineAllyson Thanks for letting me know, I'll look into that and see what's going on!
hey @Taylor Wirght, where can we find the example code? it dissapeared from the video description
👍
What is a propensity score... I am lost from there