pte_default: Default, General Function for Computing Treatment Effects...

In ptetools: Panel Treatment Effects Tools

Default, General Function for Computing Treatment Effects with Panel Data

Description

This is a generic/example wrapper for a call to the pte function.

This function provides access to difference-in-differences and unconfoundedness based identification/estimation strategies given (i) panel data and (ii) staggered treatment adoption

Usage

pte_default(
  yname,
  gname,
  tname,
  idname,
  data,
  xformula = ~1,
  d_outcome = FALSE,
  d_covs_formula = ~-1,
  lagged_outcome_cov = FALSE,
  est_method = "dr",
  anticipation = 0,
  base_period = "varying",
  control_group = "notyettreated",
  weightsname = NULL,
  cband = TRUE,
  alp = 0.05,
  boot_type = "multiplier",
  biters = 100,
  cl = 1
)

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
xformula	one-sided formula for covariates used in the propensity score and outcome regression models
d_outcome	Whether or not to take the first difference of the outcome. The default is FALSE. To use difference-in-differences, set this to be TRUE.
d_covs_formula	A formula for time varying covariates to enter the first estimation step models. The default is not to include any, and, hence, to only include pre-treatment covariates.
lagged_outcome_cov	Whether to include the lagged outcome as a covariate. Default is FALSE.
est_method	Which type of estimation method to use. Default is "dr" for doubly robust. The other option is "reg" for regression adjustment.
anticipation	how many periods before the treatment actually takes place that it can have an effect on outcomes
base_period	The type of base period to use. This only affects the numeric value of results in pre-treatment periods. Results in post-treatment periods are not affected by this choice. The default is "varying", where the base period will "back up" to the immediately preceding period in pre-treatment periods. The other option is "universal" where the base period is fixed in pre-treatment periods to be the period right before the treatment starts. "Universal" is commonly used in difference-in-differences applications, but can be unnatural for other identification strategies.
control_group	Which group is used as the comparison group. The default choice is "notyettreated", but different estimation strategies can implement their own choices for the control group
weightsname	The name of the column that contains sampling weights. The default is NULL, in which case no sampling weights are used.
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.
biters	number of bootstrap iterations; default is 100
cl	number of clusters to be used when bootstrapping; default is 1

Value

pte_results object

Examples

# example using minimum wage data
# and a lagged outcome unconfoundedness strategy
library(did)
data(mpdta)
lou_res <- pte_default(
  yname = "lemp",
  gname = "first.treat",
  tname = "year",
  idname = "countyreal",
  data = mpdta,
  xformula = ~lpop,
  d_outcome = FALSE,
  d_covs_formula = ~lpop,
  lagged_outcome_cov = TRUE
)

summary(lou_res)
ggpte(lou_res)

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