Performs Difference-in-Differences (DID) estimation.
Usage
fastdid(
data,
timevar,
cohortvar,
unitvar,
outcomevar,
control_option = "both",
result_type = "group_time",
balanced_event_time = NA,
control_type = "ipw",
allow_unbalance_panel = FALSE,
boot = FALSE,
biters = 1000,
cband = FALSE,
alpha = 0.05,
weightvar = NA,
clustervar = NA,
covariatesvar = NA,
varycovariatesvar = NA,
copy = TRUE,
validate = TRUE,
anticipation = 0,
base_period = "universal",
exper = NULL,
full = FALSE,
parallel = FALSE,
cohortvar2 = NA,
event_specific = TRUE,
double_control_option = "both"
)Arguments
- data
data.table, the dataset.
- timevar
character, name of the time variable.
- cohortvar
character, name of the cohort (group) variable.
- unitvar
character, name of the unit (id) variable.
- outcomevar
character vector, name(s) of the outcome variable(s).
- control_option
character, control units used for the DiD estimates, options are "both", "never", or "notyet".
- result_type
character, type of result to return, options are "group_time", "time", "group", "simple", "dynamic" (time since event), "group_group_time", or "dynamic_stagger".
- balanced_event_time
number, max event time to balance the cohort composition.
- control_type
character, estimator for controlling for covariates, options are "ipw" (inverse probability weighting), "reg" (outcome regression), or "dr" (doubly-robust).
- allow_unbalance_panel
logical, allow unbalance panel as input or coerce dataset into one.
- boot
logical, whether to use bootstrap standard error.
- biters
number, bootstrap iterations. Default is 1000.
- cband
logical, whether to use uniform confidence band or point-wise.
- alpha
number, the significance level. Default is 0.05.
- weightvar
character, name of the weight variable.
- clustervar
character, name of the cluster variable.
- covariatesvar
character vector, names of time-invariant covariate variables.
- varycovariatesvar
character vector, names of time-varying covariate variables.
- copy
logical, whether to copy the dataset.
- validate
logical, whether to validate the dataset.
- anticipation
number, periods with anticipation.
- base_period
character, type of base period in pre-preiods, options are "universal", or "varying".
- exper
list, arguments for experimental features.
- full
logical, whether to return the full result (influence function, call, weighting scheme, etc,.).
- parallel
logical, whether to use parallization on unix system.
- cohortvar2
character, name of the second cohort (group) variable.
- event_specific
logical, whether to recover target treatment effect or use combined effect.
- double_control_option
character, control units used for the double DiD, options are "both", "never", or "notyet".
Value
A data.table containing the estimated treatment effects and standard errors or a list of all results when `full == TRUE`.
Details
`balanced_event_time` is only meaningful when `result_type == "dynamic`.
`result_type` as `group-group-time` and `dynamic staggered` is only meaningful when using double did.
`biter` and `clustervar` is only used when `boot == TRUE`.
Examples
# simulated data
simdt <- sim_did(1e+02, 10, cov = "cont", second_cov = TRUE, second_outcome = TRUE, seed = 1)
dt <- simdt$dt
#basic call
result <- fastdid(data = dt, timevar = "time", cohortvar = "G",
unitvar = "unit", outcomevar = "y",
result_type = "group_time")