I'm attempting to apply Chaisemartin and D’Haultfoeuille's twowayfeweights command to check the weights on a DiD regression with two-way fixed effects, and wanted to check my intuition.
When I run:
which is for a first difference model, I get the output I'm expecting,
But as I have more than 2 time periods, and a large number of groups (about 2200) relative to time periods (4) I'm using a fixed effects regression instead of first differences. But when I run
which is the fixed effects model, I get
as my output. Since the coefficients would be the same under FE and FD, am I correct in thinking that I would get the same results for the LATE weights, and thus don't need to worry that I'm only getting weights for one of them? If not, does anyone know why I'm getting this weird output for the FE model? Thanks!
When I run:
Code:
twowayfeweights Y G T D, type(fds)
Under the common trends, treatment monotonicity, and if groups' treatment effect does not change over time,
beta estimates a weighted sum of 8708 LATEs.
2912 LATEs receive a positive weight, and 5796 receive a negative weight.
The sum of the positive weights is equal to 1.234136.
The sum of the negative weights is equal to -.23413596.
beta is compatible with a DGP where the average of those LATEs is equal to 0,
while their standard deviation is equal to .4681197.
beta is compatible with a DGP where those LATEs all are of a different sign than beta,
while their standard deviation is equal to 2.4338346.
beta estimates a weighted sum of 8708 LATEs.
2912 LATEs receive a positive weight, and 5796 receive a negative weight.
The sum of the positive weights is equal to 1.234136.
The sum of the negative weights is equal to -.23413596.
beta is compatible with a DGP where the average of those LATEs is equal to 0,
while their standard deviation is equal to .4681197.
beta is compatible with a DGP where those LATEs all are of a different sign than beta,
while their standard deviation is equal to 2.4338346.
Code:
twowayfeweights Y G T D, type(fes)
Under the common trends, treatment monotonicity, and if groups' treatment effect does not change over time,
beta estimates a weighted sum of 0 LATEs.
0 LATEs receive a positive weight, and 0 receive a negative weight.
The sum of the positive weights is equal to ..
The sum of the negative weights is equal to ..
beta is compatible with a DGP where the average of those LATEs is equal to 0,
while their standard deviation is equal to ..
beta estimates a weighted sum of 0 LATEs.
0 LATEs receive a positive weight, and 0 receive a negative weight.
The sum of the positive weights is equal to ..
The sum of the negative weights is equal to ..
beta is compatible with a DGP where the average of those LATEs is equal to 0,
while their standard deviation is equal to ..
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