pte: Panel Treatment Effects

In bcallaway11/pte: Panel Treatment Effects

Panel Treatment Effects

Description

Tools for estimating treatment effects with panel data

Main function for computing panel treatment effects

Usage

pte(
  yname,
  gname,
  tname,
  idname,
  data,
  setup_pte_fun,
  subset_fun,
  attgt_fun,
  cband = TRUE,
  alp = 0.05,
  boot_type = "multiplier",
  weightsname = NULL,
  biters = 100,
  cl = 1,
  gt_type = "att",
  ret_quantile = NULL,
  ...
)

Arguments

yname	Name of outcome in data
gname	Name of group in data
tname	Name of time period in data
idname	Name of id in data
data	balanced panel data
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.
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.
alp	significance level; default is 0.05
boot_type	should be one of "multiplier" (the default) or "empirical". The multiplier bootstrap is generally much faster, but attgt_fun needs to provide an expression for the influence function (which could be challenging to figure out). If no influence function is provided, then the pte package will use the empirical bootstrap no matter what the value of this parameter.
biters	number of bootstrap iterations; default is 100
cl	number of clusters to be used when bootstrapping; default is 1
gt_type	is the type of result that is computed for each group and time period. The default choice is "att" is this is (by far) the most common choice. The other option is "dtt", which stands for distribution treatment effect on the treated. In this case, the attgt_fun should return a vector of counterfactual outcomes for each unit in the data, from which a counterfactual distribution can be computed. Additional arguments will often need to be provided in this case.
...	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

pte_results object

Author(s)

Maintainer: Brantly Callaway [e-mail]

See Also

Useful links:

• https://github.com/bcallaway11/pte

bcallaway11/pte documentation built on June 6, 2024, 1:41 p.m.