R/Test_aSPA.R

In MultiHorizonSPA: Multi Horizon Superior Predictive Ability

#' Test average Superior Predictive Ability
#' 
#' Implements the test for average Superior Predictive Ability (aSPA) of Quaedvlieg (2021)
#' @param LossDiff the T x H matrix forecast path loss differential
#' @param weights the 1 x H vector of weights for the losses at different horizons. For instance \code{weights <- matlab::ones(1,20)/20}
#' @param L integer, the parameter for the moving block bootstrap
#' @param B integer, the number of bootstrap iterations. Default 999
#' @return A list containing two objects:
#' \item{"p_value"}{the p-value for aSPA}
#' \item{"t_aSPA"}{the statistics for aSPA}
#' @references Quaedvlieg, Rogier. "Multi-horizon forecast comparison." Journal of Business & Economic Statistics 39.1 (2021): 40-53.
#' @seealso \code{\link{Test_uSPA}}
#' @examples
#' ## Test for aSPA and uSPA
#' data(LossDiff_aSPA)
#' weights <- matlab::ones(1,20)/20
#' Test_aSPA(LossDiff=LossDiff_aSPA, weights=weights, L=3, B=10)
#' 
#' @author Luca Barbaglia \url{https://lucabarbaglia.github.io/}
#' 
#' @export



Test_aSPA <- function(LossDiff, weights, L, B=999){
  if (!is.matrix(LossDiff)){LossDiff <- as.matrix(LossDiff)}
  bootout <- Bootstrap_aSPA( LossDiff, weights, L, B)
  p_value <- mean(bootout$t_aSPA < bootout$t_aSPA_b)
  return(list("p_value"=p_value, "t_aSPA"=bootout$t_aSPA))
}