staggered_sa: Calculate the Sun & Abraham (2020) estimator for staggered...

In staggered: Efficient Estimation Under Staggered Treatment Timing

Calculate the Sun & Abraham (2020) estimator for staggered rollouts

Description

This functions calculates the Sun & Abraham (2020) estimator for staggered rollout designs using last-treated-treated units (never-treated, if availabe) as controls.

Usage

staggered_sa(
  df,
  i = "i",
  t = "t",
  g = "g",
  y = "y",
  estimand = NULL,
  A_theta_list = NULL,
  A_0_list = NULL,
  eventTime = 0,
  return_full_vcv = FALSE,
  compute_fisher = FALSE,
  num_fisher_permutations = 500,
  skip_data_check = FALSE
)

Arguments

df	A data frame containing panel data with the variables y (an outcome), i (an individual identifier), t (the period in which the outcome is observe), g (the period in which i is first treated, with Inf denoting never treated)
i	The name of column containing the individual (cross-sectional unit) identifier. Default is "i".
t	The name of the column containing the time periods. Default is "t".
g	The name of the column containing the first period when a particular observation is treated, with Inf denoting never treated. Default is "g".
y	The name of the column containing the outcome variable. Default is "y".
estimand	The estimand to be calculated: "simple" averages all treated (t,g) combinations with weights proportional to N_g; "cohort" averages the ATEs for each cohort g, and then takes an N_g-weighted average across g; "calendar" averages ATEs for each time period, weighted by N_g for treated units, and then averages across time. "eventstudy" returns the average effect at the ”event-time” given in the parameter EventTime. The parameter can be left blank if a custom parameter is provided in A_theta_list. The argument is not case-sensitive.
A_theta_list	This parameter allows for specifying a custom estimand, and should be left as NULL if estimand is specified. It is a list of matrices A_theta_g so that the parameter of interest is sum_g A_theta_g Ybar_g, where Ybar_g = 1/N sum_i Y_i(g)
A_0_list	This parameter allow for specifying the matrices used to construct the Xhat vector of pre-treatment differences. If left NULL, the default is to use the scalar set of controls used in Callaway and Sant'Anna. If use_DiD_A0 = FALSE, then it uses the full vector possible comparisons of (g,g') in periods t<g,g'.
eventTime	If using estimand = "eventstudy", specify what eventTime you want the event-study parameter for. The default is 0, the period in which treatment occurs. If a vector is provided, estimates are returned for all the event-times in the vector.
return_full_vcv	If this is true and estimand = "eventstudy", then the function returns a list containing the full variance-covariance matrix for the event-plot estimates in addition to the usual dataframe with the estimates
compute_fisher	If true, computes a Fisher Randomization Test using the studentized estimator.
num_fisher_permutations	The number of permutations to use in the Fisher Randomization Test (if compute_fisher = TRUE). Default is 500.
skip_data_check	If true, skips checks that the data is balanced and contains the colums i,t,g,y. Used in internal recursive calls to increase speed, but not recommended for end-user.

Value

resultsDF A data.frame containing: estimate (the point estimate), se (the standard error), and se_neyman (the Neyman standard error). If a vector-valued eventTime is provided, the data.frame contains multiple rows for each eventTime and an eventTime column. If return_full_vcv = TRUE and estimand = "eventstudy", the function returns a list containing resultsDF and the full variance covariance for the event-study estimates (vcv) as well as the Neyman version of the covariance matrix (vcv_neyman).

References

Sun, Liyang, and Abraham, Sarah (2020), 'Estimating dynamic treatment effects in event studies with heterogeneous treatment effects', Forthcoming at the Journal of Econometrics, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.jeconom.2020.09.006")}.

Examples

set.seed(1234)
# load the officer data and subset it
df <- pj_officer_level_balanced
group_random <- sample(unique(df$assigned), 3)
df <- df[df$assigned %in% group_random,]
# We modify the data so that the time dimension is named t,
# the period of treatment is named g,
# the outcome is named y,
# and the individual identifiers are named i
#  (this allow us to use default arguments on \code{staggered_cs}).
oldnames <- c("period", "complaints", "first_trained", "uid")
names(df)[match(oldnames, names(df))] <- c("t", "y", "g", "i")
# Calculate Sun and Abraham estimator for the simple weighted average
staggered_sa(df = df, estimand = "simple")
# Calculate Sun and Abraham estimator for the cohort weighted average
staggered_sa(df = df, estimand = "cohort")
# Calculate Sun and Abraham estimator for the calendar weighted average
staggered_sa(df = df, estimand = "calendar")
# Calculate Sun and Abraham event-study coefficients for the first 24 months
# (month 0 is instantaneous effect)
eventPlotResults <- staggered_sa(df = df, estimand = "eventstudy", eventTime = 0:23)
head(eventPlotResults)

staggered documentation built on Sept. 24, 2024, 5:07 p.m.