Discover and read the best of Twitter Threads about #MetricsMonday
Most recents (1)
Since it is Monday, it's time for the 'metrics paper of the week! #MetricsMonday
Today I want to highlight "Bounds on Distributional Treatment Effect Parameters using Panel Data with an Application on Job Displacement", by Brantly Callaway.
Link here: bcallaway11.github.io/files/DTE/dte1…
Today I want to highlight "Bounds on Distributional Treatment Effect Parameters using Panel Data with an Application on Job Displacement", by Brantly Callaway.
Link here: bcallaway11.github.io/files/DTE/dte1…
Brant's paper provide a set of tools that allow us to better understand heterogeneous effects in a diff-in-diff setups.
Brant is particular interested in the distribution *of* the treatment effects for the treated units, and the quantile *of* treatment effect for the treated.
Brant is particular interested in the distribution *of* the treatment effects for the treated units, and the quantile *of* treatment effect for the treated.
These parameters are much harder to identify than the popular ATT.
In fact, even if you have data from a *perfect* RCT, you will not be able to point identify these distributional parameters without relying on additional restrictions on the DGP.
In fact, even if you have data from a *perfect* RCT, you will not be able to point identify these distributional parameters without relying on additional restrictions on the DGP.
