BlockBootstrapp {EWS} | R Documentation |
Block Bootstrapp
Description
This function enables the estimation of the block size for resampling. The size of the blocks is computed as in Hall, Horowitz and Jing (1995). Then, the function returns in a matrix the new resampled input variables. These variables are then used to determine the confidence intervals of the response functions proposed by Lajaunie (2021).
Usage
BlockBootstrapp(Dicho_Y, Exp_X, Intercept, n_simul)
Arguments
Dicho_Y |
Vector of the binary time series. |
Exp_X |
Vector or Matrix of explanatory time series. |
Intercept |
Boolean value: TRUE for an estimation with intercept, and FALSE otherwise. |
n_simul |
Numeric variable equal to the total number of replications. |
Value
A matrix containing the replications of the new resampled input variables. The matrix contains colomns, where
denotes the number of input variables, and
denotes the number of replications.
Author(s)
Jean-Baptiste Hasse and Quentin Lajaunie
References
Hall, Peter, Joel L. Horowitz, and Bing-Yi Jing. "On blocking rules for the bootstrap with dependent data." Biometrika 82.3 (1995): 561-574.
Lajaunie, Quentin. Generalized Impulse Response Function for Dichotomous Models. No. 2852. Orleans Economics Laboratory/Laboratoire d'Economie d'Orleans (LEO), University of Orleans, 2021.
Examples
# NOT RUN {
# Import data
data("data_USA")
# Data process
Var_Y <- as.vector(data_USA$NBER)
Var_X <- as.vector(data_USA$Spread)
# Resample
results <- BlockBootstrapp(Dicho_Y = Var_Y, Exp_X = Var_X, Intercept = TRUE, n_simul = 100)
# print results
results
#}