Hi all,
Consider the following panel regression codes: (id: hospital ID, county: the county inwhich hospital resides, $y: global regressand, i.treatment = indicator for difference-in-dinfference)
As you can imagine, there are a significant number of singleton observations that is dropped in (1).
In (2), because I examine the county-level fixed effects, much fewer singleton observations are dropped.
In (3), since the lowest entity level for FE is hospital id, it drops just as many singletons dropped in (1).
In theory, if there are time-invariant characteristics that are important to be absorbed by the within estimator, I should always resort to (2) or (3) despite it drops a lot of singleton observations, correct?
Consider the following panel regression codes: (id: hospital ID, county: the county inwhich hospital resides, $y: global regressand, i.treatment = indicator for difference-in-dinfference)
Code:
xtreg $y i.treatment, fe vce(cluster id) xtreg $y i.treatment, fe vce(cluster county) reghdfe $y i.treatment, absorbe(id county) vce(cluster id)
In (2), because I examine the county-level fixed effects, much fewer singleton observations are dropped.
In (3), since the lowest entity level for FE is hospital id, it drops just as many singletons dropped in (1).
In theory, if there are time-invariant characteristics that are important to be absorbed by the within estimator, I should always resort to (2) or (3) despite it drops a lot of singleton observations, correct?
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