DiSCo_per_iter: DiSCo_per_iter

In DiSCos: Distributional Synthetic Controls Estimation

DiSCo_per_iter

Description

This function performs one iteration of the permutation test

Usage

DiSCo_per_iter(
  c_df,
  c_df.q,
  t_df,
  T0,
  peridx,
  evgrid,
  idx,
  grid_df,
  M = 1000,
  ww = 0,
  qmethod = NULL,
  qtype = 7,
  q_min = 0,
  q_max = 1,
  simplex = FALSE,
  mixture = FALSE
)

Arguments

c_df	List of control units
c_df.q	List of quantiles of control units
t_df	List of target unit
idx	Index of permuted target unit
grid_df	Grids to evaluate CDFs on, only needed when mixture=TRUE
M	Integer indicating the number of control quantiles to use in the DiSCo method. Default is 1000.
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.
q_min	Numeric, minimum quantile to use. Set this together with q_max to restrict the range of quantiles used to construct the synthetic control. Default is 0 (all quantiles). Currently NOT implemented for the mixture approach.
q_max	Numeric, maximum quantile to use. Set this together with q_min to restrict the range of quantiles used to construct the synthetic control. Default is 1 (all quantiles). Currently NOT implemented for the mixture approach.
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.
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.

Value

List of squared Wasserstein distances between the target unit and the control units

DiSCos documentation built on Sept. 11, 2024, 6:11 p.m.