compute.pte

Function that actually computes panel treatment effects. The difference relative to compute.pte is that this function loops over time periods first (instead of groups) and tries to estimate model for untreated potential outcomes jointly for all groups.

Usage

compute.pte(ptep, subset_fun, attgt_fun, ...)

Arguments

ptep

pte_params object

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.

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.

...

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)

Value

list of attgt results and, sometimes, an influence function