Hi all Statalisters
I'm eagerly updating myself on the new features of Stata 16 and it looks really great!
I'm interested in extended regression models (ERM) for panel data as I'm working on panel data analyses with a binary endogenous treatment. I think the instrumental variable (IV) design may be suitable. However, IV requires important assumptions (AIR '96) so I'm also interested in alternatives in case some conditions are violated.
From what I gather, ERM differs from IV (or encompasses IV and other approaches), so I'm working myself through Stata ERM Reference Manual 16 to find an explanation of the statistics behind ERM other than IV for my purpose. I'm specifically interested in the details of how ERM handles endogenous treatment assignment.
For example, briefly, the IV approach require some form of plausible exogenous variation that predict treatment with no other paths to the outcome other than through treatment, and we often use the 2SLS-estimator to obtain our estimate. I can't find some form of equivalent explanation for ERM, and I'm curious to know whether exogenous variation is a criteria for ERM or if non-random assignment is handled some other way. I can't seem to find details on this which may be due to me misunderstanding ERMs as something separate. I would highly appreciate input on this and references on the topic.
I'm eagerly updating myself on the new features of Stata 16 and it looks really great!
I'm interested in extended regression models (ERM) for panel data as I'm working on panel data analyses with a binary endogenous treatment. I think the instrumental variable (IV) design may be suitable. However, IV requires important assumptions (AIR '96) so I'm also interested in alternatives in case some conditions are violated.
From what I gather, ERM differs from IV (or encompasses IV and other approaches), so I'm working myself through Stata ERM Reference Manual 16 to find an explanation of the statistics behind ERM other than IV for my purpose. I'm specifically interested in the details of how ERM handles endogenous treatment assignment.
For example, briefly, the IV approach require some form of plausible exogenous variation that predict treatment with no other paths to the outcome other than through treatment, and we often use the 2SLS-estimator to obtain our estimate. I can't find some form of equivalent explanation for ERM, and I'm curious to know whether exogenous variation is a criteria for ERM or if non-random assignment is handled some other way. I can't seem to find details on this which may be due to me misunderstanding ERMs as something separate. I would highly appreciate input on this and references on the topic.
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