attgt.list |
list of attgt results from compute.pte
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ptep |
pte_params object
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setup_pte_fun |
This is a function that should take in data ,
yname (the name of the outcome variable in data ),
gname (the name of the group variable),
idname (the name of the id variable),
and possibly other arguments such as the significance level alp ,
the number of bootstrap iterations biters , and how many clusters
for parallel computing in the bootstrap cl . The key thing that
needs to be figured out in this function is which groups and time periods
ATT(g,t) should be computed in. The function should
return a pte_params object which contains all of the parameters
passed into the function as well as glist and tlist which
should be ordered lists of groups and time periods for ATT(g,t) to be computed.
This function provides also provides a good place for error handling related
to the types of data that can be handled.
The pte package contains the function setup_pte that is
a lightweight function that basically just takes the data, omits
the never-treated group from glist but includes all other groups
and drops the first time period. This works in cases where ATT would
be identified in the 2x2 case (i.e., where there are two time periods,
no units are treated in the first period and the identification strategy
"works" with access to a treated and untreated group and untreated
potential outcomes for both groups in the first period) — for example,
this approach works if DID is the identification strategy.
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subset_fun |
This is a function that should take in data ,
g (for group), tp (for time period), and ...
and be able to return the appropriate data.frame that can be used
by attgt_fun to produce ATT(g=g,t=tp). The data frame should
be constructed using gt_data_frame in order to guarantee that
it has the appropriate columns that identify which group an observation
belongs to, etc.
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attgt_fun |
This is a function that should work in the case where
there is a single group and the "right" number of time periods to
recover an estimate of the ATT. For example, in the contest of
difference in differences, it would need to work for a single group,
find the appropriate comparison group (untreated units), find the right
time periods (pre- and post-treatment), and then recover an estimate
of ATT for that group. It will be called over and over separately
by groups and by time periods to compute ATT(g,t)'s.
The function needs to work in a very specific way. It should take in the
arguments: data , ... . data should be constructed
using the function gt_data_frame which checks to make sure
that data has the correct columns defined.
... are additional arguments (such as
formulas for covariates) that attgt_fun needs. From these arguments
attgt_fun must return a list with element ATT containing the
group-time average treatment effect for that group and that time period.
If attgt_fun returns an influence function (which should be provided
in a list element named inf_func ), then the code will use the
multiplier bootstrap to compute standard errors for group-time average
treatment effects, an overall treatment effect parameter, and a dynamic
treatment effect parameter (i.e., event study parameter). If
attgt_fun does not return an influence function, then the same
objects will be computed using the empirical bootstrap. This is usually
(perhaps substantially) easier to code, but also will usually be (perhaps
substantially) computationally slower.
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A place to return anything extra from particular
group-time average treatment effect calculations. For DID, this might
be something like propensity score estimates, regressions of untreated
potential outcomes on covariates. For ife, this could be something
like the first step regression 2sls estimates. This argument is also
potentially useful for debugging.
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... |
extra arguments that can be passed to create the correct subsets
of the data (depending on subset_fun ), to estimate group time
average treatment effects (depending on attgt_fun ), or to
aggregating treatment effects (particularly useful are min_e ,
max_e , and balance_e arguments to event study aggregations)
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