pte: Panel Treatment Effects

In ptetools: Panel Treatment Effects Tools

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,
  gt_type = "att",
  ret_quantile = NULL,
  global_fun = FALSE,
  time_period_fun = FALSE,
  group_fun = FALSE,
  process_dtt_gt_fun = process_dtt_gt,
  process_dose_gt_fun = process_dose_gt,
  biters = 100,
  cl = 1,
  call = 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.
cband	whether or not to report a uniform (instead of pointwise) confidence band (default is TRUE)
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.
weightsname	The name of the column that contains sampling weights. The default is NULL, in which case no sampling weights are used.
gt_type	which type of group-time effects are computed. The default is "att". Different estimation strategies can implement their own choices for gt_type
ret_quantile	For functions that compute quantile treatment effects, this is a specific quantile at which to report results, e.g., ret_quantile = 0.5 will return that the qte at the median.
global_fun	Logical indicating whether or not untreated potential outcomes can be estimated in one shot, i.e., for all groups and time periods. Main use case would be for one-shot imputation estimators. Not supported yet.
time_period_fun	Logical indicating whether or not untreated potential outcomes can be estimated for all groups in the same time period. Not supported yet.
group_fun	Logical indicating whether or not untreated potential outcomes can be estimated for all time periods for a single group. Not supported yet. These functions aim at reducing or eliminating running the same code multiple times.
process_dtt_gt_fun	An optional function to customize results when the gt-specific function returns the distribution of treated and untreated potential outcomes. The default is process_dtt_gt, which is a function provided by the package. See that function for an example of what this function should return. This is unused is unused except in cases where the results involve distributions.
process_dose_gt_fun	An optional function to customize results when the gt-specific function returns treatment effects that depend on dose (i.e., amount of the treatment). The default is process_dose_gt, which is a function provided by the package. See that function for an example of what this function should return. This is unused except in cases where the results involve doses.
biters	number of bootstrap iterations; default is 100
cl	number of clusters to be used when bootstrapping; default is 1
call	keeps track of through the call from external functions/packages
...	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/ptetools

• Report bugs at https://github.com/bcallaway11/ptetools/issues

Examples

# example using minimum wage data
# and difference-in-differences identification strategy
library(did)
data(mpdta)
did_res <- pte(
  yname = "lemp",
  gname = "first.treat",
  tname = "year",
  idname = "countyreal",
  data = mpdta,
  setup_pte_fun = setup_pte,
  subset_fun = two_by_two_subset,
  attgt_fun = did_attgt,
  xformla = ~lpop
)

summary(did_res)
ggpte(did_res)

ptetools documentation built on April 12, 2025, 1:28 a.m.