DiSCo_CI: DiSCo_CI
In DiSCos: Distributional Synthetic Controls Estimation

DiSCo_CI

R Documentation

DiSCo_CI

Description

Function for computing the confidence intervals in the DiSCo method using the bootstrap approach described in

Usage

DiSCo_CI(
  redraw,
  controls,
  target,
  T_max,
  T0,
  grid,
  mc.cores = 1,
  evgrid = seq(from = 0, to = 1, length.out = 1001),
  qmethod = NULL,
  qtype = 7,
  M = 1000,
  mixture = FALSE,
  simplex = FALSE,
  replace = TRUE
)

Arguments

`redraw`	Integer indicating the current bootstrap redraw
`controls`	A list containing the raw data for the control group
`target`	A list containing the raw data for the target group
`T_max`	Index of last time period
`T0`	Index of the last pre-treatment period
`grid`	Grid to recompute the CDF on if `mixture` option is chosen
`mc.cores`	Number of cores to use for parallelization
`qmethod`	Character, indicating the method to use for computing the quantiles of the target distribution. The default is NULL, which uses the `quantile` function from the stats package. Other options are "`qkden`" (based on smoothed kernel density function) and "`extreme`" (based on parametric extreme value distributions). Both are substantially slower than the default method but may be useful for fat-tailed distributions with few data points at the upper quantiles. Alternatively, one could use the q_max option to restrict the range of quantiles used.
`qtype`	Integer, indicating the type of quantile to compute when using `quantile` in the `qmethod` argument. The default 7. See the documentation for the `quantile` function for more information.
`M`	Integer indicating the number of control quantiles to use in the DiSCo method. Default is 1000.
`mixture`	Logical, indicating whether to use the mixture of distributions approach instead. See Section 4.3. in \insertCitegunsilius2023distributional;textualDiSCos. This approach minimizes the distance between the CDFs instead of the quantile functions, and is preferred for categorical variables. When working with such variables, one should also provide a list of support points in the `grid.cat` parameter. When that is provided, this parameter is automatically set to TRUE. Default is FALSE.
`simplex`	Logical, indicating whether to use to constrain the optimal weights to the unit simplex. Default is FALSE, which only constrains the weights to sum up to 1 but allows them to be negative.
`replace`	Logical, indicating whether to sample with replacement when computing the bootstrap samples. Default is TRUE.

Value

A list with the following components

weights The bootstrapped weights
disco_boot A list containing the bootstrapped counterfactuals, with the following elements, each of which contains named elements called upper and lower which are G x T matrices where G is the specified number of grid points and T is the number of time periods