Usage
test_qte_significance(x, y, tau = 0.5, n_permutations = 1000)
Arguments
- x
Source time series
- y
Target time series
- tau
Quantile level
- n_permutations
Number of permutations for test
Value
List containing test statistics and p-value
Examples
if (FALSE) { # \dontrun{
x <- rnorm(100)
y <- 0.5 * x + rnorm(100, sd = 0.1)
test_result <- test_qte_significance(x, y)
} # }