| DiD {DiDforBigData} | R Documentation |
Estimate DiD for all possible cohorts and event time pairs (g,e), as well as the average across cohorts for each event time (e).
DiD(
inputdata,
varnames,
control_group = "all",
base_event = -1,
min_event = NULL,
max_event = NULL,
Esets = NULL,
return_ATTs_only = TRUE,
parallel_cores = 1
)
inputdata |
A data.table. |
varnames |
A list of the form varnames = list(id_name, time_name, outcome_name, cohort_name), where all four arguments of the list must be a character that corresponds to a variable name in inputdata. |
control_group |
There are three possibilities: control_group="never-treated" uses the never-treated control group only; control_group="future-treated" uses those units that will receive treatment in the future as the control group; and control_group="all" uses both the never-treated and the future-treated in the control group. Default is control_group="all". |
base_event |
This is the base pre-period that is normalized to zero in the DiD estimation. Default is base_event=-1. |
min_event |
This is the minimum event time (e) to estimate. Default is NULL, in which case, no minimum is imposed. |
max_event |
This is the maximum event time (e) to estimate. Default is NULL, in which case, no maximum is imposed. |
Esets |
If a list of sets of event times is provided, it will loop over those sets, computing the average ATT_e across event times e. Default is NULL. |
return_ATTs_only |
Return only the ATT estimates and sample sizes. Default is TRUE. |
parallel_cores |
Number of cores to use in parallel processing. If greater than 1, it will try to run library(parallel), so the "parallel" package must be installed. Default is 1. |
A list with two components: results_cohort is a data.table with the DiDge estimates (by event e and cohort g), and results_average is a data.table with the DiDe estimates (by event e, average across cohorts g). If the Esets argument is specified, a third component called results_Esets will be included in the list of output.
# simulate some data
simdata = SimDiD(sample_size=200, ATTcohortdiff = 2)$simdata
# define the variable names as a list()
varnames = list()
varnames$time_name = "year"
varnames$outcome_name = "Y"
varnames$cohort_name = "cohort"
varnames$id_name = "id"
# estimate the ATT for all cohorts at event time 1 only
DiD(simdata, varnames, min_event=1, max_event=1)