Dear All,
I am recently learning Oster(2016)'s approach for robustness checks with respect to unobservable selection bias. Specifically, I am using psacalc for this practice. And I have two questions when implementing this:
(1) Negative Delta
When calculating the delta for beta=0, the command I use is:
and I got values of delta being negative (-0.3 and in some regressions -4). But as Oster(2016)'s major analysis is taking care of the situation where delta is assumed to be positive, so I'm confused about the output and how to interpret the negative delta case.
(A similar post is: http://www.statalist.org/forums/foru...e-of-selection, and it is not answered yet. )
(2) Use psacalc with DID setup
Now I'm trying to use a DID specification with psacalc. My regression is the following:
Here the treatment is at group level c and is continuous(like a dosage). Post_t is a dummy indicator indicating post-treatment period, and ID_c is group identifier. With the DID specification, I've controlled the group FE and post dummy, and the variable of interest now is an interaction term: Treatment*Post.
So when using psacalc to calculate the adjusted beta of this interaction term, I guess we should use: "psacalc beta Treatment*Post, mcontrols(ID_c, Post Dummy)", instead of "psacalc beta Treatment*Post"?
(Is there any examples, e.g. in papers, that have used this approach in DID or RD settings?)
Many thanks!!!
Lingwei
I am recently learning Oster(2016)'s approach for robustness checks with respect to unobservable selection bias. Specifically, I am using psacalc for this practice. And I have two questions when implementing this:
(1) Negative Delta
When calculating the delta for beta=0, the command I use is:
psacalc delta variable_of_interest
and I got values of delta being negative (-0.3 and in some regressions -4). But as Oster(2016)'s major analysis is taking care of the situation where delta is assumed to be positive, so I'm confused about the output and how to interpret the negative delta case.
(A similar post is: http://www.statalist.org/forums/foru...e-of-selection, and it is not answered yet. )
(2) Use psacalc with DID setup
Now I'm trying to use a DID specification with psacalc. My regression is the following:
areg Y_ct Treatment_c*Post_t Post_t, absorb(ID_c)
Here the treatment is at group level c and is continuous(like a dosage). Post_t is a dummy indicator indicating post-treatment period, and ID_c is group identifier. With the DID specification, I've controlled the group FE and post dummy, and the variable of interest now is an interaction term: Treatment*Post.
So when using psacalc to calculate the adjusted beta of this interaction term, I guess we should use: "psacalc beta Treatment*Post, mcontrols(ID_c, Post Dummy)", instead of "psacalc beta Treatment*Post"?
(Is there any examples, e.g. in papers, that have used this approach in DID or RD settings?)
Many thanks!!!
Lingwei
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